Compacted table data files validation

ABSTRACT

Database replay log compaction verification includes identifying at least one replay log of a table that includes first database manipulation commands; obtaining a compacted replay log that includes second database manipulation commands that are insert commands, where an insert command includes a column and a corresponding value; replaying, to obtain a first replay result, the first database manipulation commands; replaying, to obtain a second replay result, the second database manipulation commands; and, responsive to one row of the first replay result not matching a corresponding row of the second replay result, sending a notification including a non-match. Replaying the first database manipulation commands includes identifying condition columns of the table; responsive to the condition columns not including the column, obtaining a row corresponding to the insert command, where the row includes a modified value of the corresponding value of the column; and adding the row to the first replay result.

BACKGROUND

Advances in computer storage and database technology have led toexponential growth of the amount of data being created. Businesses areoverwhelmed by the volume of the data stored in their computer systems.Existing database analytic tools are inefficient, costly to utilize,and/or require substantial configuration and training.

SUMMARY

Disclosed herein are implementations of compacted table data filesvalidation.

A first aspect is a method for database replay log compactionverification. The method includes identifying at least one replay log ofa table, the at least one replay log includes first databasemanipulation commands; obtaining a compacted replay log of the table,the compacted replay log includes second database manipulation commands,where the compacted replay log only includes insert commands, and wherean insert command of the first database manipulation commands includes acolumn and a corresponding value for the column; replaying, to obtain afirst replay result, the first database manipulation commands;replaying, to obtain a second replay result, the second databasemanipulation commands; and, responsive to one row of the first replayresult not matching a corresponding row of the second replay result,sending a notification including a non-match. Replaying the firstdatabase manipulation commands includes identifying condition columns ofthe table, where the condition columns are used in at least onecondition of the first database manipulation commands; responsive to thecondition columns not including the column, obtaining a rowcorresponding to the insert command, where the row includes a modifiedvalue of the corresponding value of the column; and adding the row tothe first replay result.

A second aspect is a method for database replay log compactionverification. The method includes replaying a first replay log togenerate a first replay result, where the first replay log includescommands for obtaining an in-memory database; replaying a second replaylog to generate a second replay result; and comparing the first replayresult and the second replay result to verify that the first replay logand the second replay log are equivalent. Replaying the first replay logincludes replacing a first value of a first field included in a firstcommand in the first replay log with a first hash value responsive to adetermination that the first field is not utilized as a condition in atleast one command included in the first replay log.

A third aspect is a method for replay log compaction verification. Themethod includes replaying, to obtain a first replay result, firstdatabase manipulation commands of at least one replay log, where thefirst database manipulation commands include at least one of an updatecommand or a delete command; replaying, to obtain a second replayresult, second database manipulation commands of a compacted replay log,where the compacted replay log consists of insert commands and where thecompacted replay log is a compacted version of the first replay log thathas been generated using a compaction process; and, responsive to a rowof the first replay result not matching a corresponding row of thesecond replay result, sending a notification indicating a non-matchcorresponding to the row of the first replay result.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosure is best understood from the following detaileddescription when read in conjunction with the accompanying drawings. Itis emphasized that, according to common practice, the various featuresof the drawings are not to-scale. On the contrary, the dimensions of thevarious features are arbitrarily expanded or reduced for clarity.

FIG. 1 is a block diagram of an example of a computing device.

FIG. 2 is a block diagram of an example of a computing system.

FIG. 3 is a block diagram of an example of a low-latency databaseanalysis system.

FIG. 4 is a flowchart of an example of a technique for validatingcompacted replay logs.

FIG. 5 illustrates an example of replay logs and a compacted replay logresulting therefrom.

FIG. 6 illustrates a first replay result and a second replay result.

FIG. 7 is a flowchart of an example of a technique for database replaylog verification.

FIG. 8 is a flowchart of an example of a technique for database replaylog compaction verification.

DETAILED DESCRIPTION

Businesses and other organizations store large amounts of data, such asbusiness records, transaction records, and the like, in data storagesystems, such as relational database systems that store data as records,or rows, having values, or fields, corresponding to respective columnsin tables that can be interrelated using key values. Databasesstructures are often normalized or otherwise organized to maximize datadensity and to maximize transactional data operations at the expense ofincreased complexity and reduced accessibility for analysis. Individualrecords and tables may have little or no utility without substantialcorrelation, interpretation, and analysis. The complexity of these datastructures and the large volumes of data that can be stored thereinlimit the accessibility of the data and require substantial skilledhuman resources to code procedures and tools that allow business usersto access useful data. The tools that are available for accessing thesesystems are limited to outputting data expressly requested by the usersand lack the capability to identify and prioritize data other than thedata expressly requested. Useful data, such as data aggregations,patterns, and statistical anomalies that would not be available insmaller data sets (e.g., 10,000 rows of data), and may not be apparentto human users, may be derivable using the large volume of data (e.g.,millions or billions of rows) stored in complex data storage systems,such as relational database systems, and may be inaccessible due to thecomplexity and limitations of the data storage systems.

In a database system, such as a low-latency database analysis systemdescribed herein, different mechanisms (e.g., tools, processes, etc.)may be available for restoring at least a portion of the database (e.g.,data of the database, low-latency data of a distributed in-memorydatabase of the low-latency database analysis system) to a previousstate. It may be desirable to restore the database to a previous statein cases of database system crashes; volatile memory (e.g., low-latencymemory) failures where, for example, the volatile memory includes dataof the in-memory database; permanent storage failures; erroneous logic(e.g., programming, etc.) that may have introduced data errors into thedatabase; erroneous data being loaded into the database; or any othercause that may necessitate the restoration of the database to a previousstate. In an example, as the low-latency memory may be volatile, thelow-latency data of the in-memory database may be backed up (e.g.,persisted) in one or more non-volatile storage units (such as asolid-state drive (SSD), hard disk drive (HDD), a distributed filesystem (DFS), and/or the like). The in-memory state of the low-latencydatabase can be reconstituted from the persisted state.

Scheduled or manual tasks may be performed to obtain full backups of thedatabase, which may include backing up all of the data of the database,configuration settings of the database system, replay logs (which mayalso be referred to as transaction logs). A full backup represents thedatabase system at the time that the backup is obtained. A full backupmay be used to reconstitute (e.g., replicate, move, restart, etc.) thedatabase system in the same or a different environment (e.g., machine,physical server, virtual server, cluster of servers, etc.) than that ofthe database system. Additionally, scheduled or manual tasks may beperformed to obtain snapshots of the database. A snapshot includes thedata, state, and metadata of the database system created since a lastsnapshot creation. A snapshot may be restored to the same, but notanother, environment the snapshot was created on.

The database system may maintain replay logs that include databasemanipulation commands (e.g., statements, directives, instructions,etc.). The database manipulation commands can include data manipulationcommands, data definition commands, or both. Data manipulation commandscan be used to modify existing data of the database, add (e.g., insert)new data into the database, or delete data from the database. Datadefinition commands can be used to create, modify, or delete databaseobjects, such as data that describes one or more aspects of theattributes, rows, columns, tables, relationships, indices, or otheraspects of the data or database.

The database system can maintain one or more replay logs: the databasesystem can append all received manipulation commands to one replay log;the database system can maintain one or more replay logs for each tableof the database; and/or the database system can maintain version chainsof replay logs. Maintaining replay logs can include more, fewer, otheractivity/processes, or a combination thereof.

To recover to a current state of the database, a latest full backup orsnapshot may be restored and the commands of the replay logs accumulatedsince the latest full backup or snapshot can be replayed (e.g.,re-executed, re-preformed, re-applied, etc.) on the restored latest fullbackup or snapshot. In an example, the snapshot may itself be stored inthe format of replay logs. That is, the snapshot can be one or morereplay logs that include commands for recreating all the data of thedatabase. The database system may maintain replay logs on a per-tablebasis.

Replay logs may be versioned such that each set of commands that arereceived and processed by the database system for a table can constitutea version. As such, replay logs can be stored in the form of versionchains. For ease of reference a set of commands that are received by thedatabase system for processing and are processed by the database systemis referred to herein as a loading event. The database system caninclude data (e.g., metadata, etc.) indicating respective versions ofthe tables of the database. After every load event for a table of thedatabase system, the version of the table may be incremented by 1. Assuch, a version x of a table can be built on top of version x−1 of thetable. When a next loading event occurs, version x+1 of the table iscreated on top of version x using only the commands (i.e., the data ofthe commands) of the load event. Loading events may be processed (e.g.,handled, executed, carried out, etc.) by an enterprise data interfaceunit of the low-latency database analysis system.

As further described herein, the in-memory database maintainslow-latency data in low-latency memory. In a case that the in-memorydatabase is restarted (such as due to a change in configuration, ahardware failure, an algorithmic change of the in-memory database, orsome other reason), the data of a table can be reconstituted (e.g.,reloaded, etc.) in the low-latency memory from the replay logscorresponding to the table. For example, assume that a table named“Animals” (which is defined to include the three columns ID, NAME, andSOUND) was subject to ten loading events, where each of the loadingevents may affect one or more rows or columns of table. As such, tenreplay logs (which may be numbered or named replayLog-Animals-0 toreplayLog-Animals-9) may be associated with the Animals table. Toreconstitute the low-latency data for the Animals table, the versionchain of the replay logs are sequentially processed fromreplayLog-Animals-0 to replayLog-Animals-9.

The version chain of the replay logs for a table may become too long(e.g., may include too many replay logs) such that processing all thereplay logs of the version chain consumes significant amounts ofcomputational resources (e.g., processor time, memory, clock time,etc.). Additionally, storing the replay logs may require a significantamount of storage. The storage requirements may be compounded when thereplay logs are stored to a DFS, which may create multiple copies of thereplay logs. However, the use of such computational resources may bewasteful at least because the replay logs may include redundant,obsolete commands, or otherwise supplanted commands. For example, acommand may be redundant if it updates a field to be the same as apreviously set value of that field or a group of commands may beredundant if they insert and then delete the same row(s) in the table.As a further example, a command that inserts a first value may beobsolete if that first value is later changed to a second value andremoval of the command does not affect the final state of the databaseonce the replay logs are processed.

Furthermore, replaying the commands of a replay logs may be complex(e.g., require complex algorithms, etc.) or time consuming. For example,replaying delete, update, schema update, and UPSERT commands may requirecomplex logic. UPSERT refers to the following scenario. When a table iscreated, one or more columns may be designated as primary-key columns.That is, for every row in the table, the combination of the primary-keycolumns values is required to be unique. When a new row is inserted, ifthe primary-key columns values for the new row is the same as anexisting row, a database (e.g., an in-memory database) may mark the oldrow as deleted and inserts a new row.

High computational resource utilization can degrade the performance ofthe database system and may cause some operations to fail due toresource exhaustion. The possibility for degraded performance may alsoinclude or require substantially increased investment in processing,memory, and storage resources and may also result in increased energyexpenditures (needed to operate those increased processing, memory, andstorage resources, and for network transmissions) and associatedemissions that may result from the generation of that energy.Additionally, highly complex algorithms are likely to include logicerrors, which may result is data corruption that in turn degrade thereliability and utility of the database system.

The database system may compact the replay logs in a version chain toreduce the computational resource utilization and/or computationalcomplexity. For example, to enable faster data reloads from replay logsor to reduce the disk space usage of replay logs, the database systemcan compact the replay logs by reducing the number of commands that mayneed to be performed, such as by consolidating (e.g., removing,ignoring, merging, etc.) obsolete, redundant, or otherwise supplantedcommands. For example, the replay logs described above may be compactedinto a single file, which may be partially named, for example,Animals_0-9.compacted, signifying that the versions 0-9 of the versionchain have been compacted into this single file. To reconstitute thelow-latency data of the Animals table, the database system need onlyprocess (e.g., replay) the file Animals_0-10.compacted. In an example,the compacted file (i.e., compacted replay log) only includes INSERTcommands. To illustrate, a command of the form INSERT (x) followed by acommand of the form UPDATE (x with y) can be compacted to a singlecommand INSERT (y) therewith eliminating the UPDATE command altogether.Thus, compaction includes writing the in-memory state of the low-latencydata to a persistent store and removing long version chains by writingthe data back to disk as a single version that includes only insertcommands.

Compaction may be triggered (e.g., initiated, etc.) manually orautomatically (such as on a schedule or based on a determination that atable is eligible for compaction). To illustrate, and withoutlimitations, the database system may determine that a table (e.g., theAnimals table) is eligible for compaction in response to determiningthat the in-memory size (e.g., 100 megabyte) of the table is smallerthan the combined size (e.g., 300 megabytes) of the replay logsassociated with the table. Other criteria or heuristics may be used todetermine the eligibility of a table for compaction.

As a first illustration of compaction, assume that a loading eventincludes a first command “Insert into Animals the row (ID=1, NAME=‘COW’,SOUND=‘MEOW’)” and a later loading event includes a second command“delete from Animals where ID=1.” As such, performing the second commandcancels the effect of the first command. The insert command followed bythe delete command on the same row is effectively a no-operation (no-op)command. Therefore, it would preserve resources to omit both commands inthe compacted file (i.e., compacted replay log). As a secondillustration, assume that a loading event includes a third command“Insert into Animals the row (ID=1, NAME=‘COW’, SOUND=‘MEOW’)” and alater loading event includes a fourth command “update Animals setSOUND=‘MOO’ WHERE NAME=‘COW’.” As such, instead of performing the thirdand fourth commands, the third and fourth commands can be compacted intothe single command “Insert into Animals the row (ID=1, NAME=‘COW’,SOUND=‘MOO’).” Therefore, it would preserve resources to compact thethird and fourth commands into one command.

After compaction of replay logs, the replay logs may be deleted as theyare no longer needed. However, data of the database (e.g., low-latencydata) may have become corrupted prior to the compaction. For examples,algorithmic changes to the database system or memory corruption mayresult in data corruption. To illustrate using but a simple example,whereas a received command of a loading event may have been “Insert intoAnimals the row (ID=1, NAME=‘COW’, SOUND=‘MEOW’),” the database systemmay have inserted the row (ID=7, NAME=‘COW’, SOUND=‘MEOW’) in theAnimals table. In another example, the compaction instructions mayinclude erroneous logic such that the insert commands written to thecompacted file may insert data that are not according to the commands inthe replay logs. For example, the compaction instructions may not fullytake into account corner cases e.g., certain rare conditions that mayexist in certain replay logs-which may result in data errors in thecompacted replay logs.

As such, during compaction corrupted data may be written into persistentstorage (i.e., the compacted file) and the correct information(contained in the replay logs) may no longer be recoverable if theoriginal non-compacted replay logs are deleted. A possible solution tomitigate this problem is to periodically obtain backups and restore datafrom the backups if a problem occurs. However, taking periodic backupsis inefficient because backups require a significant amount of space andtakes significant time to complete. Additionally, restoring from abackup may not solve the problem because it may not be possible torecover to a latest state of the data from a backup as a backup may notinclude the latest data. Furthermore, there may not be an easy way, ifat all, to identify whether and when (e.g., prior to or before whichbackup) a data corruption problem actually occurred.

Therefore, to protect the integrity of the data in a database system, itis critical to validate that first data that would result from replayingthe replay logs are equivalent to second data that would result fromreplaying the compacted replay log corresponding to the replay logs.Prior to using (such as to restore low-latency data of an in-memorydatabase or a portion thereof) the compacted replay log (instead of thereplay logs), it is necessary to validate the compacted replay log.

Implementations according to this disclosure are designed to helpprotect the integrity of data in a database system, such as theintegrity of low-latency data of an in-memory database, by validatingthat replay logs and a compacted file therefrom result in the same dataand protecting against data loss (such as when replay logs are deletedtherewith eliminating the possibility of data recovery as describedherein); can reduce the need for frequent backups or snapshots, whichmay consume a significant amount of storage space, therewith resultingin a reduction of storage space; can detect data corruption when resultsof the validation do not match therewith initiating (such as by adatabase administrator) an investigation of the source of, and aresolution to, the corruption.

Validation and/or compaction according to this disclosure can beperformed in a manner that optimizes usage of computational resources.For example, with respect to compute resources, validation can beperformed with respect to one table at a time, with respect to one shard(e.g., table region) of an in-memory database at a time, with respect toall shards or tables of an in-memory database at a time, or with respectto some other granularity, using low-priority processes, using onecompute thread, or a combination thereof. As such, the performance(e.g., query performance, data analysis performance, etc.) of thedatabase system is not degraded during validation and/or compaction.While, for ease of understanding, the disclosure is mainly describedwith respect to compacting and validating replay logs associated with atable, the disclosure is not so limited and compaction and validationcan, for example, also be performed with respect to one or more shardsof a table. Thus, the term “table,” and unless the context indicatesotherwise, should be understood to encompass, for a table that issharded, all shards of the table or one or more shards of the table.

Validating the compacted replay log can include replaying the replaylogs to obtain a first replay result; replaying the compacted replay logto obtain a second replay result; and comparing the first replay resultto the second replay result.

In a conventional approach, replaying the replay logs to obtain thefirst replay result may reproduce data of the database exactly as thedata are stored in the database. Similarly, replaying the compactedreplay log to obtain the second replay result, and assuming no errors ordata corruption, may reproduce the data of the database exactly as thedata are stored in the database. As such, the replaying of the replaylogs and/or the compacted replay log would consume a significant amountof memory (permanent or volatile, whatever the case may be). However,reproducing the data exactly as stored in the database is not necessaryand is, therefore, wasteful.

Contrastingly, replaying the replay logs to obtain the first replayresult according to implementations of this disclosure can modifycertain data to be added to first replay result and/or the second replayresult to reduce the memory used by the first replay result. A modifieddatum (i.e., a modified value) of a datum (i.e., a value) that is in thedatabase can be a representation of the datum that takes less space(e.g., fewer number of bits or bytes) than the datum itself.

For example, with respect to column values that are used in commands ofthe replay log and that are of type string, hash values of at least someof those string values can be added to the first replay result insteadof the values themselves. For example, and as can be appreciated,respective fixed-sized (e.g., 8-byte) hash values of large strings mayrequire significantly less storage space than the strings themselves.Whether to add a column (e.g., string) value itself or a hash of thecolumn value depends on whether the column is used in a conditional(e.g., a test, etc.) in a subsequent command of the replay logs. Ashashing cannot be reversed, if a column is identified as a conditioncolumn, then the string values of the column are not hashed. On theother hand, if a column is not identified as a condition column, thenall values of the column can be hashed. As such, to obtain the firstreplay result of replaying the replay logs, conditions columns of thecommands of the replay logs are first identified. When adding data (e.g.a row) to or updating data of (e.g., modifying a column value of a row)the first replay result, the values of any columns of the added orupdated data that are identified to be conditions columns are stored inthe data of the first replay result without modification.

As mentioned above, the compacted replay log includes only INSERTcommands. As such, no command of the compacted replay log includesconditional columns. Therefore, in an example, all values of all columnsof type string can be stored in the second replay result as hashedvalues. In an example, each row of the second replay result can be ahash value. For example, all string values of a row can be hashed andconcatenated with the values of the other columns to obtain a largeconcatenated string, wherein the order of the columns is preserved inthe concatenated string. A hash of the large string is can be obtainedand written to the second replay result. Values of the second replayresult can be hashed in a way that is consistent with the hashing of thefirst replay result so that the first replay result and the secondreplay result are comparable.

To compare the first replay result to the second replay result, hashesof the rows of the first replay result can be compared to respectivehashes of rows of the second replay result. In an example, a hash of arow of the first replay result can be obtained by hashing string valuesof columns that are not already hashed, concatenating the hashed valueswith the values of the other columns to obtain a large concatenatedstring, wherein the order of the columns is preserved in the largeconcatenated string. A hash of the large concatenated string can then beobtained. Other ways of obtaining hashes of rows are possible, asfurther described herein.

While compaction is used to explain the concepts of this disclosure, thetechniques described herein can be used generally to compare at leasttwo sets of replay logs (referred to herein as first replay log(s) andsecond replay log(s)). The techniques described herein can be usedwith/for any database system operation(s) that may write in-memory datainto database manipulation commands file (i.e., second replay logs) andtrigger a delete of original database manipulation commands files (i.e.,first replay logs). The techniques described herein can be used todetermine whether a first set of commands (e.g., first replay log(s)) isor is likely to be equivalent to a second set of commands (e.g., secondreplay log(s)). Equivalence in this context means that the first set ofcommand and the second set of commands, when replayed, produce the samedata.

FIG. 1 is a block diagram of an example of a computing device 1000. Oneor more aspects of this disclosure may be implemented using thecomputing device 1000. The computing device 1000 includes a processor1100, static memory 1200, low-latency memory 1300, an electroniccommunication unit 1400, a user interface 1500, a bus 1600, and a powersource 1700. Although shown as a single unit, any one or more element ofthe computing device 1000 may be integrated into any number of separatephysical units. For example, the low-latency memory 1300 and theprocessor 1100 may be integrated in a first physical unit and the userinterface 1500 may be integrated in a second physical unit. Although notshown in FIG. 1 , the computing device 1000 may include other aspects,such as an enclosure or one or more sensors.

The computing device 1000 may be a stationary computing device, such asa personal computer (PC), a server, a workstation, a minicomputer, or amainframe computer; or a mobile computing device, such as a mobiletelephone, a personal digital assistant (PDA), a laptop, or a tablet PC.

The processor 1100 may include any device or combination of devicescapable of manipulating or processing a signal or other information,including optical processors, quantum processors, molecular processors,or a combination thereof. The processor 1100 may be a central processingunit (CPU), such as a microprocessor, and may include one or moreprocessing units, which may respectively include one or more processingcores. The processor 1100 may include multiple interconnectedprocessors. For example, the multiple processors may be hardwired ornetworked, including wirelessly networked. In some implementations, theoperations of the processor 1100 may be distributed across multiplephysical devices or units that may be coupled directly or across anetwork. In some implementations, the processor 1100 may include acache, or cache memory, for internal storage of operating data orinstructions. The processor 1100 may include one or more special purposeprocessors, one or more digital signal processor (DSP), one or moremicroprocessors, one or more controllers, one or more microcontrollers,one or more integrated circuits, one or more an Application SpecificIntegrated Circuits, one or more Field Programmable Gate Array, one ormore programmable logic arrays, one or more programmable logiccontrollers, firmware, one or more state machines, or any combinationthereof.

The processor 1100 may be operatively coupled with the static memory1200, the low-latency memory 1300, the electronic communication unit1400, the user interface 1500, the bus 1600, the power source 1700, orany combination thereof. The processor may execute, which may includecontrolling, such as by sending electronic signals to, receivingelectronic signals from, or both, the static memory 1200, thelow-latency memory 1300, the electronic communication unit 1400, theuser interface 1500, the bus 1600, the power source 1700, or anycombination thereof to execute, instructions, programs, code,applications, or the like, which may include executing one or moreaspects of an operating system, and which may include executing one ormore instructions to perform one or more aspects described herein, aloneor in combination with one or more other processors.

The static memory 1200 is coupled to the processor 1100 via the bus 1600and may include non-volatile memory, such as a disk drive, or any formof non-volatile memory capable of persistent electronic informationstorage, such as in the absence of an active power supply. Althoughshown as a single block in FIG. 1 , the static memory 1200 may beimplemented as multiple logical or physical units.

The static memory 1200 may store executable instructions or data, suchas application data, an operating system, or a combination thereof, foraccess by the processor 1100. The executable instructions may beorganized into programmable modules or algorithms, functional programs,codes, code segments, or combinations thereof to perform one or moreaspects, features, or elements described herein. The application datamay include, for example, user files, database catalogs, configurationinformation, or a combination thereof. The operating system may be, forexample, a desktop or laptop operating system; an operating system for amobile device, such as a smartphone or tablet device; or an operatingsystem for a large device, such as a mainframe computer.

The low-latency memory 1300 is coupled to the processor 1100 via the bus1600 and may include any storage medium with low-latency data accessincluding, for example, DRAM modules such as DDR SDRAM, Phase-ChangeMemory (PCM), flash memory, or a solid-state drive. Although shown as asingle block in FIG. 1 , the low-latency memory 1300 may be implementedas multiple logical or physical units. Other configurations may be used.For example, low-latency memory 1300, or a portion thereof, andprocessor 1100 may be combined, such as by using a system on a chipdesign.

The low-latency memory 1300 may store executable instructions or data,such as application data for low-latency access by the processor 1100.The executable instructions may include, for example, one or moreapplication programs, that may be executed by the processor 1100. Theexecutable instructions may be organized into programmable modules oralgorithms, functional programs, codes, code segments, and/orcombinations thereof to perform various functions described herein.

The low-latency memory 1300 may be used to store data that is analyzedor processed using the systems or methods described herein. For example,storage of some or all data in low-latency memory 1300 instead of staticmemory 1200 may improve the execution speed of the systems and methodsdescribed herein by permitting access to data more quickly by an orderof magnitude or greater (e.g., nanoseconds instead of microseconds).

The electronic communication unit 1400 is coupled to the processor 1100via the bus 1600. The electronic communication unit 1400 may include oneor more transceivers. The electronic communication unit 1400 may, forexample, provide a connection or link to a network via a networkinterface. The network interface may be a wired network interface, suchas Ethernet, or a wireless network interface. For example, the computingdevice 1000 may communicate with other devices via the electroniccommunication unit 1400 and the network interface using one or morenetwork protocols, such as Ethernet, Transmission ControlProtocol/Internet Protocol (TCP/IP), power line communication (PLC),Wi-Fi, infrared, ultra violet (UV), visible light, fiber optic, wireline, general packet radio service (GPRS), Global System for Mobilecommunications (GSM), code-division multiple access (CDMA), Long-TermEvolution (LTE), or other suitable protocols.

The user interface 1500 may include any unit capable of interfacing witha human user, such as a virtual or physical keypad, a touchpad, adisplay, a touch display, a speaker, a microphone, a video camera, asensor, a printer, or any combination thereof. For example, a keypad canconvert physical input of force applied to a key to an electrical signalthat can be interpreted by computing device 1000. In another example, adisplay can convert electrical signals output by computing device 1000to light. The purpose of such devices may be to permit interaction witha human user, for example by accepting input from the human user andproviding output back to the human user. The user interface 1500 mayinclude a display; a positional input device, such as a mouse, touchpad,touchscreen, or the like; a keyboard; or any other human and machineinterface device. The user interface 1500 may be coupled to theprocessor 1100 via the bus 1600. In some implementations, the userinterface 1500 can include a display, which can be a liquid crystaldisplay (LCD), a cathode-ray tube (CRT), a light emitting diode (LED)display, an organic light emitting diode (OLED) display, an activematrix organic light emitting diode (AMOLED), or other suitable display.In some implementations, the user interface 1500, or a portion thereof,may be part of another computing device (not shown). For example, aphysical user interface, or a portion thereof, may be omitted from thecomputing device 1000 and a remote or virtual interface may be used,such as via the electronic communication unit 1400.

The bus 1600 is coupled to the static memory 1200, the low-latencymemory 1300, the electronic communication unit 1400, the user interface1500, and the power source 1700. Although a single bus is shown in FIG.1 , the bus 1600 may include multiple buses, which may be connected,such as via bridges, controllers, or adapters.

The power source 1700 provides energy to operate the computing device1000. The power source 1700 may be a general-purpose alternating-current(AC) electric power supply, or power supply interface, such as aninterface to a household power source. In some implementations, thepower source 1700 may be a single use battery or a rechargeable batteryto allow the computing device 1000 to operate independently of anexternal power distribution system. For example, the power source 1700may include a wired power source; one or more dry cell batteries, suchas nickel-cadmium (NiCad), nickel-zinc (NiZn), nickel metal hydride(NiMH), lithium-ion (Li-ion); solar cells; fuel cells; or any otherdevice capable of powering the computing device 1000.

FIG. 2 is a block diagram of an example of a computing system 2000. Asshown, the computing system 2000 includes an external data sourceportion 2100, an internal database analysis portion 2200, and a systeminterface portion 2300. The computing system 2000 may include otherelements not shown in FIG. 2 , such as computer network elements.

The external data source portion 2100 may be associated with, such ascontrolled by, an external person, entity, or organization(second-party). The internal database analysis portion 2200 may beassociated with, such as created by or controlled by, a person, entity,or organization (first-party). The system interface portion 2300 may beassociated with, such as created by or controlled by, the first-partyand may be accessed by the first-party, the second-party, third-parties,or a combination thereof, such as in accordance with access andauthorization permissions and procedures.

The external data source portion 2100 is shown as including externaldatabase servers 2120 and external application servers 2140. Theexternal data source portion 2100 may include other elements not shownin FIG. 2 . The external data source portion 2100 may include externalcomputing devices, such as the computing device 1000 shown in FIG. 1 ,which may be used by or accessible to the external person, entity, ororganization (second-party) associated with the external data sourceportion 2100, including but not limited to external database servers2120 and external application servers 2140. The external computingdevices may include data regarding the operation of the external person,entity, or organization (second-party) associated with the external datasource portion 2100.

The external database servers 2120 may be one or more computing devicesconfigured to store data in a format and schema determined externallyfrom the internal database analysis portion 2200, such as by asecond-party associated with the external data source portion 2100, or athird party. For example, the external database server 2120 may use arelational database and may include a database catalog with a schema. Insome embodiments, the external database server 2120 may include anon-database data storage structure, such as a text-based datastructure, such as a comma separated variable structure or an extensiblemarkup language formatted structure or file. For example, the externaldatabase servers 2120 can include data regarding the production ofmaterials by the external person, entity, or organization (second-party)associated with the external data source portion 2100, communicationsbetween the external person, entity, or organization (second-party)associated with the external data source portion 2100 and third parties,or a combination thereof. Other data may be included. The externaldatabase may be a structured database system, such as a relationaldatabase operating in a relational database management system (RDBMS),which may be an enterprise database. In some embodiments, the externaldatabase may be an unstructured data source. The external data mayinclude data or content, such as sales data, revenue data, profit data,tax data, shipping data, safety data, sports data, health data, weatherdata, or the like, or any other data, or combination of data, that maybe generated by or associated with a user, an organization, or anenterprise and stored in a database system. For simplicity and clarity,data stored in or received from the external data source portion 2100may be referred to herein as enterprise data.

The external application server 2140 may include application software,such as application software used by the external person, entity, ororganization (second-party) associated with the external data sourceportion 2100. The external application server 2140 may include data ormetadata relating to the application software.

The external database servers 2120, the external application servers2140, or both, shown in FIG. 2 may represent logical units or devicesthat may be implemented on one or more physical units or devices, whichmay be controlled or operated by the first party, the second party, or athird party.

The external data source portion 2100, or aspects thereof, such as theexternal database servers 2120, the external application servers 2140,or both, may communicate with the internal database analysis portion2200, or an aspect thereof, such as one or more of the servers 2220,2240, 2260, and 2280, via an electronic communication medium, which maybe a wired or wireless electronic communication medium. For example, theelectronic communication medium may include a local area network (LAN),a wide area network (WAN), a fiber channel network, the Internet, or acombination thereof.

The internal database analysis portion 2200 is shown as includingservers 2220, 2240, 2260, and 2280. The servers 2220, 2240, 2260, and2280 may be computing devices, such as the computing device 1000 shownin FIG. 1 . Although four servers 2220, 2240, 2260, and 2280 are shownin FIG. 2 , other numbers, or cardinalities, of servers may be used. Forexample, the number of computing devices may be determined based on thecapability of individual computing devices, the amount of data to beprocessed, the complexity of the data to be processed, or a combinationthereof. Other metrics may be used for determining the number ofcomputing devices.

The internal database analysis portion 2200 may store data, processdata, or store and process data. The internal database analysis portion2200 may include a distributed cluster (not expressly shown) which mayinclude two or more of the servers 2220, 2240, 2260, and 2280. Theoperation of distributed cluster, such as the operation of the servers2220, 2240, 2260, and 2280 individually, in combination, or both, may bemanaged by a distributed cluster manager. For example, the server 2220may be the distributed cluster manager. In another example, thedistributed cluster manager may be implemented on another computingdevice (not shown). The data and processing of the distributed clustermay be distributed among the servers 2220, 2240, 2260, and 2280, such asby the distributed cluster manager.

Enterprise data from the external data source portion 2100, such as fromthe external database server 2120, the external application server 2140,or both may be imported into the internal database analysis portion2200. The external database server 2120, the external application server2140, or both may be one or more computing devices and may communicatewith the internal database analysis portion 2200 via electroniccommunication. The imported data may be distributed among, processed by,stored on, or a combination thereof, one or more of the servers 2220,2240, 2260, and 2280. Importing the enterprise data may includeimporting or accessing the data structures of the enterprise data.Importing the enterprise data may include generating internal data,internal data structures, or both, based on the enterprise data. Theinternal data, internal data structures, or both may accuratelyrepresent and may differ from the enterprise data, the data structuresof the enterprise data, or both. In some implementations, enterprisedata from multiple external data sources may be imported into theinternal database analysis portion 2200. For simplicity and clarity,data stored or used in the internal database analysis portion 2200 maybe referred to herein as internal data. For example, the internal data,or a portion thereof, may represent, and may be distinct from,enterprise data imported into or accessed by the internal databaseanalysis portion 2200.

The system interface portion 2300 may include one or more client devices2320, 2340. The client devices 2320, 2340 may be computing devices, suchas the computing device 1000 shown in FIG. 1 . For example, one of theclient devices 2320, 2340 may be a desktop or laptop computer and theother of the client devices 2320, 2340 may be a mobile device,smartphone, or tablet. One or more of the client devices 2320, 2340 mayaccess the internal database analysis portion 2200. For example, theinternal database analysis portion 2200 may provide one or moreservices, application interfaces, or other electronic computercommunication interfaces, such as a web site, and the client devices2320, 2340 may access the interfaces provided by the internal databaseanalysis portion 2200, which may include accessing the internal datastored in the internal database analysis portion 2200.

In an example, one or more of the client devices 2320, 2340 may send amessage or signal indicating a request for data, which may include arequest for data analysis, to the internal database analysis portion2200. The internal database analysis portion 2200 may receive andprocess the request, which may include distributing the processing amongone or more of the servers 2220, 2240, 2260, and 2280, may generate aresponse to the request, which may include generating or modifyinginternal data, internal data structures, or both, and may output theresponse to the client device 2320, 2340 that sent the request.Processing the request may include accessing one or more internal dataindexes, an internal database, or a combination thereof. The clientdevice 2320, 2340 may receive the response, including the response dataor a portion thereof, and may store, output, or both, the response or arepresentation thereof, such as a representation of the response data,or a portion thereof, which may include presenting the representationvia a user interface on a presentation device of the client device 2320,2340, such as to a user of the client device 2320, 2340.

The system interface portion 2300, or aspects thereof, such as one ormore of the client devices 2320, 2340, may communicate with the internaldatabase analysis portion 2200, or an aspect thereof, such as one ormore of the servers 2220, 2240, 2260, and 2280, via an electroniccommunication medium, which may be a wired or wireless electroniccommunication medium. For example, the electronic communication mediummay include a local area network (LAN), a wide area network (WAN), afiber channel network, the Internet, or a combination thereof.

FIG. 3 is a block diagram of an example of a low-latency databaseanalysis system 3000. The low-latency database analysis system 3000, oraspects thereof, may be similar to the internal database analysisportion 2200 shown in FIG. 2 , except as described herein or otherwiseclear from context. The low-latency database analysis system 3000, oraspects thereof, may be implemented on one or more computing devices,such as servers 2220, 2240, 2260, and 2280 shown in FIG. 2 , which maybe in a clustered or distributed computing configuration.

The low-latency database analysis system 3000 may store and maintain theinternal data, or a portion thereof, such as low-latency data, in alow-latency memory device, such as the low-latency memory 1300 shown inFIG. 1 , or any other type of data storage medium or combination of datastorage devices with relatively fast (low-latency) data access,organized in a low-latency data structure. In some embodiments, thelow-latency database analysis system 3000 may be implemented as one ormore logical devices in a cloud-based configuration optimized forautomatic database analysis.

As shown, the low-latency database analysis system 3000 includes adistributed cluster manager 3100, a security and governance unit 3200, adistributed in-memory database 3300, an enterprise data interface unit3400, a distributed in-memory ontology unit 3500, a semantic interfaceunit 3600, a relational search unit 3700, a natural language processingunit 3710, a data utility unit 3720, an insight unit 3730, an objectsearch unit 3800, an object utility unit 3810, a system configurationunit 3820, a user customization unit 3830, a system access interfaceunit 3900, a real-time collaboration unit 3910, a third-partyintegration unit 3920, and a persistent storage unit 3930, which may becollectively referred to as the components of the low-latency databaseanalysis system 3000.

Although not expressly shown in FIG. 3 , one or more of the componentsof the low-latency database analysis system 3000 may be implemented onone or more operatively connected physical or logical computing devices,such as in a distributed cluster computing configuration, such as theinternal database analysis portion 2200 shown in FIG. 2 . Although shownseparately in FIG. 3 , one or more of the components of the low-latencydatabase analysis system 3000, or respective aspects thereof, may becombined or otherwise organized.

The low-latency database analysis system 3000 may include different,fewer, or additional components not shown in FIG. 3 . The aspects orcomponents implemented in an instance of the low-latency databaseanalysis system 3000 may be configurable. For example, the insight unit3730 may be omitted or disabled. One or more of the components of thelow-latency database analysis system 3000 may be implemented in a mannersuch that aspects thereof are divided or combined into variousexecutable modules or libraries in a manner which may differ from thatdescribed herein.

The low-latency database analysis system 3000 may implement anapplication programming interface (API), which may monitor, receive, orboth, input signals or messages from external devices and systems,client systems, process received signals or messages, transmitcorresponding signals or messages to one or more of the components ofthe low-latency database analysis system 3000, and output, such astransmit or send, output messages or signals to respective externaldevices or systems. The low-latency database analysis system 3000 may beimplemented in a distributed computing configuration.

The distributed cluster manager 3100 manages the operative configurationof the low-latency database analysis system 3000. Managing the operativeconfiguration of the low-latency database analysis system 3000 mayinclude controlling the implementation of and distribution of processingand storage across one or more logical devices operating on one or morephysical devices, such as the servers 2220, 2240, 2260, and 2280 shownin FIG. 2 . The distributed cluster manager 3100 may generate andmaintain configuration data for the low-latency database analysis system3000, such as in one or more tables, identifying the operativeconfiguration of the low-latency database analysis system 3000. Forexample, the distributed cluster manager 3100 may automatically updatethe low-latency database analysis system configuration data in responseto an operative configuration event, such as a change in availability orperformance for a physical or logical unit of the low-latency databaseanalysis system 3000. One or more of the component units of low-latencydatabase analysis system 3000 may access the database analysis systemconfiguration data, such as to identify intercommunication parameters orpaths.

The security and governance unit 3200 may describe, implement, enforce,or a combination thereof, rules and procedures for controlling access toaspects of the low-latency database analysis system 3000, such as theinternal data of the low-latency database analysis system 3000 and thefeatures and interfaces of the low-latency database analysis system3000. The security and governance unit 3200 may apply security at anontological level to control or limit access to the internal data of thelow-latency database analysis system 3000, such as to columns, tables,rows, or fields, which may include using row level security.

Although shown as a single unit in FIG. 3 , the distributed in-memorydatabase 3300 may be implemented in a distributed configuration, such asdistributed among the servers 2220, 2240, 2260, and 2280 shown in FIG. 2, which may include multiple in-memory database instances. Eachin-memory database instance may utilize one or more distinct resources,such as processing or low-latency memory resources, that differ from theresources utilized by the other in-memory database instances. In someembodiments, the in-memory database instances may utilize one or moreshared resources, such as resources utilized by two or more in-memorydatabase instances.

The distributed in-memory database 3300 may generate, maintain, or both,a low-latency data structure and data stored or maintained therein(low-latency data). The low-latency data may include principal data,which may represent enterprise data, such as enterprise data importedfrom an external enterprise data source, such as the external datasource portion 2100 shown in FIG. 2 . In some implementations, thedistributed in-memory database 3300 may include system internal datarepresenting one or more aspects, features, or configurations of thelow-latency database analysis system 3000. The distributed in-memorydatabase 3300 and the low-latency data stored therein, or a portionthereof, may be accessed using commands, messages, or signals inaccordance with a defined structured query language associated with thedistributed in-memory database 3300.

The low-latency data, or a portion thereof, may be organized as tablesin the distributed in-memory database 3300. A table may be a datastructure to organize or group the data or a portion thereof, such asrelated or similar data. A table may have a defined structure. Forexample, each table may define or describe a respective set of one ormore columns.

A column may define or describe the characteristics of a discrete aspectof the data in the table. For example, the definition or description ofa column may include an identifier, such as a name, for the columnwithin the table, and one or more constraints, such as a data type, forthe data corresponding to the column in the table. The definition ordescription of a column may include other information, such as adescription of the column. The data in a table may be accessible orpartitionable on a per-column basis. The set of tables, including thecolumn definitions therein, and information describing relationshipsbetween elements, such as tables and columns, of the database may bedefined or described by a database schema or design. The cardinality ofcolumns of a table, and the definition and organization of the columns,may be defined by the database schema or design. Adding, deleting, ormodifying a table, a column, the definition thereof, or a relationshipor constraint thereon, may be a modification of the database design,schema, model, or structure.

The low-latency data, or a portion thereof, may be stored in thedatabase as one or more rows or records in respective tables. Eachrecord or row of a table may include a respective field or cellcorresponding to each column of the table. A field may store a discretedata value. The cardinality of rows of a table, and the values storedtherein, may be variable based on the data. Adding, deleting, ormodifying rows, or the data stored therein may omit modification of thedatabase design, schema, or structure. The data stored in respectivecolumns may be identified or defined as a measure data, attribute data,or enterprise ontology data (e.g., metadata).

Measure data, or measure values, may include quantifiable or additivenumeric values, such as integer or floating-point values, which mayinclude numeric values indicating sizes, amounts, degrees, or the like.A column defined as representing measure values may be referred toherein as a measure or fact. A measure may be a property on whichquantitative operations (e.g., sum, count, average, minimum, maximum)may be performed to calculate or determine a result or output.

Attribute data, or attribute values, may include non-quantifiablevalues, such as text or image data, which may indicate names anddescriptions, quantifiable values designated, defined, or identified asattribute data, such as numeric unit identifiers, or a combinationthereof. A column defined as including attribute values may be referredto herein as an attribute or dimension. For example, attributes mayinclude text, identifiers, timestamps, or the like.

Enterprise ontology data may include data that defines or describes oneor more aspects of the database, such as data that describes one or moreaspects of the attributes, measures, rows, columns, tables,relationships, or other aspects of the data or database schema. Forexample, a portion of the database design, model, or schema may berepresented as enterprise ontology data in one or more tables in thedatabase.

Distinctly identifiable data in the low-latency data may be referred toherein as a data portion. For example, the low-latency data stored inthe distributed in-memory database 3300 may be referred to herein as adata portion, a table from the low-latency data may be referred toherein as a data portion, a column from the low-latency data may bereferred to herein as a data portion, a row or record from thelow-latency data may be referred to herein as a data portion, a valuefrom the low-latency data may be referred to herein as a data portion, arelationship defined in the low-latency data may be referred to hereinas a data portion, enterprise ontology data describing the low-latencydata may be referred to herein as a data portion, or any otherdistinctly identifiable data, or combination thereof, from thelow-latency data may be referred to herein as a data portion.

The distributed in-memory database 3300 may create or add one or moredata portions, such as a table, may read from or access one or more dataportions, may update or modify one or more data portions, may remove ordelete one or more data portions, or a combination thereof. Adding,modifying, or removing data portions may include changes to the datamodel of the low-latency data. Changing the data model of thelow-latency data may include notifying one or more other components ofthe low-latency database analysis system 3000, such as by sending, orotherwise making available, a message or signal indicating the change.For example, the distributed in-memory database 3300 may create or add atable to the low-latency data and may transmit or send a message orsignal indicating the change to the semantic interface unit 3600.

In some implementations, a portion of the low-latency data may representa data model of an external enterprise database and may omit the datastored in the external enterprise database, or a portion thereof. Forexample, prioritized data may be cached in the distributed in-memorydatabase 3300 and the other data may be omitted from storage in thedistributed in-memory database 3300, which may be stored in the externalenterprise database. In some implementations, requesting data from thedistributed in-memory database 3300 may include requesting the data, ora portion thereof, from the external enterprise database.

The distributed in-memory database 3300 may receive one or more messagesor signals indicating respective data-queries for the low-latency data,or a portion thereof, which may include data-queries for modified,generated, or aggregated data generated based on the low-latency data,or a portion thereof. For example, the distributed in-memory database3300 may receive a data-query from the semantic interface unit 3600,such as in accordance with a request for data. The data-queries receivedby the distributed in-memory database 3300 may be agnostic to thedistributed configuration of the distributed in-memory database 3300. Adata-query, or a portion thereof, may be expressed in accordance withthe defined structured query language implemented by the distributedin-memory database 3300. In some implementations, a data-query may beincluded, such as stored or communicated, in a data-query data structureor container.

The distributed in-memory database 3300 may execute or perform one ormore queries to generate or obtain response data responsive to thedata-query based on the low-latency data. Unless expressly described, orotherwise clear from context, descriptions herein of a table in thecontext of performing, processing, or executing a data-query thatinclude accessing, such as reading, writing, or otherwise using, atable, or data from a table, may refer to a table stored, or otherwisemaintained, in the low-latency distributed database independently of thedata-query or may refer to tabular data obtained, such as generated, inaccordance with the data-query.

The distributed in-memory database 3300 may interpret, evaluate, orotherwise process a data-query to generate one or moredistributed-queries, which may be expressed in accordance with thedefined structured query language. For example, an in-memory databaseinstance of the distributed in-memory database 3300 may be identified asa query coordinator. The query coordinator may generate a query plan,which may include generating one or more distributed-queries, based onthe received data-query. The query plan may include query executioninstructions for executing one or more queries, or one or more portionsthereof, based on the received data-query by the one or more of thein-memory database instances. Generating the query plan may includeoptimizing the query plan. The query coordinator may distribute, orotherwise make available, the respective portions of the query plan, asquery execution instructions, to the corresponding in-memory databaseinstances.

The respective in-memory database instances may receive thecorresponding query execution instructions from the query coordinator.The respective in-memory database instances may execute thecorresponding query execution instructions to obtain, process, or both,data (intermediate results data) from the low-latency data. Therespective in-memory database instances may output, or otherwise makeavailable, the intermediate results data, such as to the querycoordinator.

The query coordinator may execute a respective portion of queryexecution instructions (allocated to the query coordinator) to obtain,process, or both, data (intermediate results data) from the low-latencydata. The query coordinator may receive, or otherwise access, theintermediate results data from the respective in-memory databaseinstances. The query coordinator may combine, aggregate, or otherwiseprocess, the intermediate results data to obtain results data.

In some embodiments, obtaining the intermediate results data by one ormore of the in-memory database instances may include outputting theintermediate results data to, or obtaining intermediate results datafrom, one or more other in-memory database instances, in addition to, orinstead of, obtaining the intermediate results data from the low-latencydata.

The distributed in-memory database 3300 may output, or otherwise makeavailable, the results data to the semantic interface unit 3600.

The enterprise data interface unit 3400 may interface with, orcommunicate with, an external enterprise data system. For example, theenterprise data interface unit 3400 may receive or access enterprisedata from or in an external system, such as an external database. Theenterprise data interface unit 3400 may import, evaluate, or otherwiseprocess the enterprise data to populate, create, or modify data storedin the low-latency database analysis system 3000. The enterprise datainterface unit 3400 may receive, or otherwise access, the enterprisedata from one or more external data sources, such as the external datasource portion 2100 shown in FIG. 2 , and may represent the enterprisedata in the low-latency database analysis system 3000 by importing,loading, or populating the enterprise data as principal data in thedistributed in-memory database 3300, such as in one or more low-latencydata structures. The enterprise data interface unit 3400 may implementone or more data connectors, which may transfer data between, forexample, the external data source and the distributed in-memory database3300, which may include altering, formatting, evaluating, ormanipulating the data.

The enterprise data interface unit 3400 may receive, access, or generatemetadata that identifies one or more parameters or relationships for theprincipal data, such as based on the enterprise data, and may includethe generated metadata in the low-latency data stored in the distributedin-memory database 3300. For example, the enterprise data interface unit3400 may identify characteristics of the principal data such as,attributes, measures, values, unique identifiers, tags, links, keys, orthe like, and may include metadata representing the identifiedcharacteristics in the low-latency data stored in the distributedin-memory database 3300. The characteristics of the data can beautomatically determined by receiving, accessing, processing,evaluating, or interpreting the schema in which the enterprise data isstored, which may include automatically identifying links orrelationships between columns, classifying columns (e.g., using columnnames), and analyzing or evaluating the data.

Distinctly identifiable operative data units or structures representingone or more data portions, one or more entities, users, groups, ororganizations represented in the internal data, or one or moreaggregations, collections, relations, analytical results,visualizations, or groupings thereof, may be represented in thelow-latency database analysis system 3000 as objects. An object mayinclude a unique identifier for the object, such as a fully qualifiedname. An object may include a name, such as a displayable value, for theobject.

For example, an object may represent a user, a group, an entity, anorganization, a privilege, a role, a table, a column, a datarelationship, a worksheet, a view, a context, an answer, an insight, apinboard, a tag, a comment, a trigger, a defined variable, a datasource, an object-level security rule, a row-level security rule, or anyother data capable of being distinctly identified and stored orotherwise obtained in the low-latency database analysis system 3000. Anobject may represent or correspond with a logical entity. Datadescribing an object may include data operatively or uniquelyidentifying data corresponding to, or represented by, the object in thelow-latency database analysis system. For example, a column in a tablein a database in the low-latency database analysis system may berepresented in the low-latency database analysis system as an object andthe data describing or defining the object may include data operativelyor uniquely identifying the column.

A worksheet (worksheet object), or worksheet table, may be a logicaltable, or a definition thereof, which may be a collection, a sub-set(such as a subset of columns from one or more tables), or both, of datafrom one or more data sources, such as columns in one or more tables,such as in the distributed in-memory database 3300. A worksheet, or adefinition thereof, may include one or more data organization ormanipulation definitions, such as join paths or worksheet-columndefinitions, which may be user defined. A worksheet may be a datastructure that may contain one or more rules or definitions that maydefine or describe how a respective tabular set of data may be obtained,which may include defining one or more sources of data, such as one ormore columns from the distributed in-memory database 3300. A worksheetmay be a data source. For example, a worksheet may include references toone or more data sources, such as columns in one or more tables, such asin the distributed in-memory database 3300, and a request for datareferencing the worksheet may access the data from the data sourcesreferenced in the worksheet. In some implementations, a worksheet mayomit aggregations of the data from the data sources referenced in theworksheet.

An answer (answer object), or report, may be a defined, such aspreviously generated, request for data, such as a resolved-request. Ananswer may include information describing a visualization of dataresponsive to the request for data.

A visualization (visualization object) may be a defined representationor expression of data, such as a visual representation of the data, forpresentation to a user or human observer, such as via a user interface.Although described as a visual representation, in some implementations,a visualization may include non-visual aspects, such as auditory orhaptic presentation aspects. A visualization may be generated torepresent a defined set of data in accordance with a definedvisualization type or template (visualization template object), such asin a chart, graph, or tabular form. Example visualization types mayinclude, and are not limited to, chloropleths, cartograms, dotdistribution maps, proportional symbol maps, contour/isopleth/isarithmicmaps, daysymetric map, self-organizing map, timeline, time series,connected scatter plots, Gantt charts, steam graph/theme river, arcdiagrams, polar area/rose/circumplex charts, Sankey diagrams, alluvialdiagrams, pie charts, histograms, tag clouds, bubble charts, bubbleclouds, bar charts, radial bar charts, tree maps, scatter plots, linecharts, step charts, area charts, stacked graphs, heat maps, parallelcoordinates, spider charts, box and whisker plots, mosaic displays,waterfall charts, funnel charts, or radial tree maps. A visualizationtemplate may define or describe one or more visualization parameters,such as one or more color parameters. Visualization data for avisualization may include values of one or more of the visualizationparameters of the corresponding visualization template.

A view (view object) may be a logical table, or a definition thereof,which may be a collection, a sub-set, or both, of data from one or moredata sources, such as columns in one or more tables, such as in thedistributed in-memory database 3300. For example, a view may begenerated based on an answer, such as by storing the answer as a view. Aview may define or describe a data aggregation. A view may be a datasource. For example, a view may include references to one or more datasources, such as columns in one or more tables, such as in thedistributed in-memory database 3300, which may include a definition ordescription of an aggregation of the data from a respective data source,and a request for data referencing the view may access the aggregateddata, the data from the unaggregated data sources referenced in theworksheet, or a combination thereof. The unaggregated data from datasources referenced in the view defined or described as aggregated datain the view may be unavailable based on the view. A view may be amaterialized view or an unmaterialized view. A request for datareferencing a materialized view may obtain data from a set of datapreviously obtained (view-materialization) in accordance with thedefinition of the view and the request for data. A request for datareferencing an unmaterialized view may obtain data from a set of datacurrently obtained in accordance with the definition of the view and therequest for data.

A pinboard (pinboard object), or dashboard, may be a defined collectionor grouping of objects, such as visualizations, answers, or insights.Pinboard data for a pinboard may include information associated with thepinboard, which may be associated with respective objects included inthe pinboard.

A context (context object) may be a set or collection of data associatedwith a request for data or a discretely related sequence or series ofrequests for data or other interactions with the low-latency databaseanalysis system 3000.

A definition may be a set of data describing the structure ororganization of a data portion. For example, in the distributedin-memory database 3300, a column definition may define one or moreaspects of a column in a table, such as a name of the column, adescription of the column, a datatype for the column, or any otherinformation about the column that may be represented as discrete data.

A data source object may represent a source or repository of dataaccessible by the low-latency database analysis system 3000. A datasource object may include data indicating an electronic communicationlocation, such as an address, of a data source, connection information,such as protocol information, authentication information, or acombination thereof, or any other information about the data source thatmay be represented as discrete data. For example, a data source objectmay represent a table in the distributed in-memory database 3300 andinclude data for accessing the table from the database, such asinformation identifying the database, information identifying a schemawithin the database, and information identifying the table within theschema within the database. An external data source object may representan external data source. For example, an external data source object mayinclude data indicating an electronic communication location, such as anaddress, of an external data source, connection information, such asprotocol information, authentication information, or a combinationthereof, or any other information about the external data source thatmay be represented as discrete data.

A sticker (sticker object) may be a description of a classification,category, tag, subject area, or other information that may be associatedwith one or more other objects such that objects associated with asticker may be grouped, sorted, filtered, or otherwise identified basedon the sticker. In the distributed in-memory database 3300 a tag may bea discrete data portion that may be associated with other data portions,such that data portions associated with a tag may be grouped, sorted,filtered, or otherwise identified based on the tag.

The distributed in-memory ontology unit 3500 generates, maintains, orboth, information (ontological data) defining or describing theoperative ontological structure of the objects represented in thelow-latency database analysis system 3000, such as in the low-latencydata stored in the distributed in-memory database 3300, which mayinclude describing attributes, properties, states, or other informationabout respective objects and may include describing relationships amongrespective objects.

Objects may be referred to herein as primary objects, secondary objects,or tertiary objects. Other types of objects may be used.

Primary objects may include objects representing distinctly identifiableoperative data units or structures representing one or more dataportions in the distributed in-memory database 3300, or another datasource in the low-latency database analysis system 3000. For example,primary objects may be data source objects, table objects, columnobjects, relationship objects, or the like. Primary objects may includeworksheets, views, filters, such as row-level-security filters and tablefilters, variables, or the like. Primary objects may be referred toherein as data-objects or queryable-objects.

Secondary objects may be objects representing distinctly identifiableoperative data units or structures representing analytical dataaggregations, collections, analytical results, visualizations, orgroupings thereof, such as pinboard objects, answer objects, insights,visualization objects, and the like. Secondary objects may be referredto herein as analytical-objects.

Tertiary objects may be objects representing distinctly identifiableoperative data units or structures representing operational aspects ofthe low-latency database analysis system 3000, such as one or moreentities, users, groups, or organizations represented in the internaldata, such as user objects, user-group objects, role objects, stickerobjects, and the like.

The distributed in-memory ontology unit 3500 may represent theontological structure, which may include the objects therein, as a graphhaving nodes and edges. A node may be a representation of an object inthe graph structure of the distributed in-memory ontology unit 3500. Anode, representing an object, can include one or more components. Thecomponents of a node may be versioned, such as on a per-component basis.For example, a node can include a header component, a content component,or both. A header component may include information about the node. Acontent component may include the content of the node. An edge mayrepresent a relationship between nodes, which may be directional.

In some implementations, the distributed in-memory ontology unit 3500graph may include one or more nodes, edges, or both, representing one ormore objects, relationships or both, corresponding to a respectiveinternal representation of enterprise data stored in an externalenterprise data storage unit, wherein a portion of the data stored inthe external enterprise data storage unit represented in the distributedin-memory ontology unit 3500 graph is omitted from the distributedin-memory database 3300.

In some embodiments, the distributed in-memory ontology unit 3500 maygenerate, modify, or remove a portion of the ontology graph in responseto one or more messages, signals, or notifications from one or more ofthe components of the low-latency database analysis system 3000. Forexample, the distributed in-memory ontology unit 3500 may generate,modify, or remove a portion of the ontology graph in response toreceiving one or more messages, signals, or notifications from thedistributed in-memory database 3300 indicating a change to thelow-latency data structure. In another example, the distributedin-memory database 3300 may send one or more messages, signals, ornotifications indicating a change to the low-latency data structure tothe semantic interface unit 3600 and the semantic interface unit 3600may send one or more messages, signals, or notifications indicating thechange to the low-latency data structure to the distributed in-memoryontology unit 3500.

The distributed in-memory ontology unit 3500 may be distributed,in-memory, multi-versioned, transactional, consistent, durable, or acombination thereof. The distributed in-memory ontology unit 3500 istransactional, which may include implementing atomic concurrent, orsubstantially concurrent, updating of multiple objects. The distributedin-memory ontology unit 3500 is durable, which may include implementinga robust storage that prevents data loss subsequent to or as a result ofthe completion of an atomic operation. The distributed in-memoryontology unit 3500 is consistent, which may include performingoperations associated with a request for data with reference to or usinga discrete data set, which may mitigate or eliminate the riskinconsistent results.

The distributed in-memory ontology unit 3500 may generate, output, orboth, one or more event notifications. For example, the distributedin-memory ontology unit 3500 may generate, output, or both, anotification, or notifications, in response to a change of thedistributed in-memory ontology. The distributed in-memory ontology unit3500 may identify a portion of the distributed in-memory ontology(graph) associated with a change of the distributed in-memory ontology,such as one or more nodes depending from a changed node, and maygenerate, output, or both, a notification, or notifications indicatingthe identified relevant portion of the distributed in-memory ontology(graph). One or more aspects of the low-latency database analysis system3000 may cache object data and may receive the notifications from thedistributed in-memory ontology unit 3500, which may reduce latency andnetwork traffic relative to systems that omit caching object data oromit notifications relevant to changes to portions of the distributedin-memory ontology (graph).

The distributed in-memory ontology unit 3500 may implement prefetching.For example, the distributed in-memory ontology unit 3500 maypredictively, such as based on determined probabilistic utility, fetchone or more nodes, such as in response to access to a related node by acomponent of the low-latency database analysis system 3000.

The distributed in-memory ontology unit 3500 may implement amulti-version concurrency control graph data storage unit. Each node,object, or both, may be versioned. Changes to the distributed in-memoryontology may be reversible. For example, the distributed in-memoryontology may have a first state prior to a change to the distributedin-memory ontology, the distributed in-memory ontology may have a secondstate subsequent to the change, and the state of the distributedin-memory ontology may be reverted to the first state subsequent to thechange, such as in response to the identification of an error or failureassociated with the second state.

In some implementations, reverting a node, or a set of nodes, may omitreverting one or more other nodes. In some implementations, thedistributed in-memory ontology unit 3500 may maintain a change logindicating a sequential record of changes to the distributed in-memoryontology (graph), such that a change to a node or a set of nodes may bereverted and one or more other changes subsequent to the reverted changemay be reverted for consistency.

The distributed in-memory ontology unit 3500 may implement optimisticlocking to reduce lock contention times. The use of optimistic lockingpermits improved throughput of data through the distributed in-memoryontology unit 3500.

The semantic interface unit 3600 may implement procedures and functionsto provide a semantic interface between the distributed in-memorydatabase 3300 and one or more of the other components of the low-latencydatabase analysis system 3000.

The semantic interface unit 3600 may implement ontological datamanagement, data-query generation, authentication and access control,object statistical data collection, or a combination thereof.

Ontological data management may include object lifecycle management,object data persistence, ontological modifications, or the like. Objectlifecycle management may include creating one or more objects, readingor otherwise accessing one or more objects, updating or modifying one ormore objects, deleting or removing one or more objects, or a combinationthereof. For example, the semantic interface unit 3600 may interface orcommunicate with the distributed in-memory ontology unit 3500, which maystore the ontological data, object data, or both, to perform objectlifecycle management, object data persistence, ontologicalmodifications, or the like.

For example, the semantic interface unit 3600 may receive, or otherwiseaccess, a message, signal, or notification, such as from the distributedin-memory database 3300, indicating the creation or addition of a dataportion, such as a table, in the low-latency data stored in thedistributed in-memory database 3300, and the semantic interface unit3600 may communicate with the distributed in-memory ontology unit 3500to create an object in the ontology representing the added data portion.The semantic interface unit 3600 may transmit, send, or otherwise makeavailable, a notification, message, or signal to the relational searchunit 3700 indicating that the ontology has changed.

The semantic interface unit 3600 may receive, or otherwise access, arequest message or signal, such as from the relational search unit 3700,indicating a request for information describing changes to the ontology(ontological updates request). The semantic interface unit 3600 maygenerate and send, or otherwise make available, a response message orsignal to the relational search unit 3700 indicating the changes to theontology (ontological updates response). The semantic interface unit3600 may identify one or more data portions for indexing based on thechanges to the ontology. For example, the changes to the ontology mayinclude adding a table to the ontology, the table including multiplerows, and the semantic interface unit 3600 may identify each row as adata portion for indexing. The semantic interface unit 3600 may includeinformation describing the ontological changes in the ontologicalupdates response. The semantic interface unit 3600 may include one ormore data-query definitions, such as data-query definitions for indexingdata-queries, for each data portion identified for indexing in theontological updates response. For example, the data-query definitionsmay include a sampling data-query, which may be used to query thedistributed in-memory database 3300 for sample data from the added dataportion, an indexing data-query, which may be used to query thedistributed in-memory database 3300 for data from the added dataportion, or both.

The semantic interface unit 3600 may receive, or otherwise access,internal signals or messages including data expressing a usage intent,such as data indicating requests to access or modify the low-latencydata stored in the distributed in-memory database 3300 (e.g., a requestfor data). The request to access or modify the low-latency data receivedby the semantic interface unit 3600 may include a resolved-request. Theresolved-request, which may be database and visualization agnostic, maybe expressed or communicated as an ordered sequence of tokens, which mayrepresent semantic data. For example, the relational search unit 3700may tokenize, identify semantics, or both, based on input data, such asinput data representing user input, to generate the resolved-request.The resolved-request may include an ordered sequence of tokens thatrepresent the request for data corresponding to the input data, and maytransmit, send, or otherwise make accessible, the resolved-request tothe semantic interface unit 3600. The semantic interface unit 3600 mayprocess or respond to a received resolved-request.

The semantic interface unit 3600 may process or transform the receivedresolved-request, which may be, at least in part, incompatible with thedistributed in-memory database 3300, to generate one or morecorresponding data-queries that are compatible with the distributedin-memory database 3300, which may include generating a proto-queryrepresenting the resolved-request, generating a pseudo-queryrepresenting the proto-query, and generating the data-query representingthe pseudo-query.

The semantic interface unit 3600 may generate a proto-query based on theresolved-request. A proto-query, which may be database agnostic, may bestructured or formatted in a form, language, or protocol that differsfrom the defined structured query language of the distributed in-memorydatabase 3300. Generating the proto-query may include identifyingvisualization identification data, such as an indication of a type ofvisualization, associated with the request for data, and generating theproto-query based on the resolved-request and the visualizationidentification data.

The semantic interface unit 3600 may transform the proto-query togenerate a pseudo-query. The pseudo-query, which may be databaseagnostic, may be structured or formatted in a form, language, orprotocol that differs from the defined structured query language of thedistributed in-memory database 3300. Generating a pseudo-query mayinclude applying a defined transformation, or an ordered sequence oftransformations. Generating a pseudo-query may include incorporatingrow-level security filters in the pseudo-query.

The semantic interface unit 3600 may generate a data-query based on thepseudo-query, such as by serializing the pseudo-query. The data-query,or a portion thereof, may be structured or formatted using the definedstructured query language of the distributed in-memory database 3300. Insome implementations, a data-query may be structured or formatted usinga defined structured query language of another database, which maydiffer from the defined structured query language of the distributedin-memory database 3300. Generating the data-query may include using oneor more defined rules for expressing respective the structure andcontent of a pseudo-query in the respective defined structured querylanguage.

The semantic interface unit 3600 may communicate, or issue, thedata-query to the distributed in-memory database 3300. In someimplementations, processing or responding to a resolved-request mayinclude generating and issuing multiple data-queries to the distributedin-memory database 3300.

The semantic interface unit 3600 may receive results data from thedistributed in-memory database 3300 responsive to one or moreresolved-requests. The semantic interface unit 3600 may process, format,or transform the results data to obtain visualization data. For example,the semantic interface unit 3600 may identify a visualization forrepresenting or presenting the results data, or a portion thereof, suchas based on the results data or a portion thereof. For example, thesemantic interface unit 3600 may identifying a bar chart visualizationfor results data including one measure and attribute.

Although not shown separately in FIG. 3 , the semantic interface unit3600 may include a data visualization unit. In some embodiments, thedata visualization unit may be a distinct unit, separate from thesemantic interface unit 3600. In some embodiments, the datavisualization unit may be included in the system access interface unit3900. The data visualization unit, the system access interface unit3900, or a combination thereof, may generate a user interface, or one ormore portions thereof. For example, data visualization unit, the systemaccess interface unit 3900, or a combination thereof, may obtain theresults data, such as the visualization data, and may generate userinterface elements (visualizations) representing the results data.

The semantic interface unit 3600 may implement object-level security,row-level security, or a combination thereof. Object level security mayinclude security associated with an object, such as a table, a column, aworksheet, an answer, or a pinboard. Row-level security may includeuser-based or group-based access control of rows of data in thelow-latency data, the indexes, or both. The semantic interface unit 3600may implement on or more authentication procedures, access controlprocedures, or a combination thereof.

The semantic interface unit 3600 may implement one or more user-dataintegration features. For example, the semantic interface unit 3600 maygenerate and output a user interface, or a portion thereof, forinputting, uploading, or importing user data, may receive user data, andmay import the user data. For example, the user data may be enterprisedata.

The semantic interface unit 3600 may implement object statistical datacollection. Object statistical data may include, for respective objects,temporal access information, access frequency information, accessrecency information, access requester information, or the like. Forexample, the semantic interface unit 3600 may obtain object statisticaldata as described with respect to the data utility unit 3720, the objectutility unit 3810, or both. The semantic interface unit 3600 may send,transmit, or otherwise make available, the object statistical data fordata-objects to the data utility unit 3720. The semantic interface unit3600 may send, transmit, or otherwise make available, the objectstatistical data for analytical-objects to the object utility unit 3810.

The semantic interface unit 3600 may implement or expose one or moreservices or application programming interfaces. For example, thesemantic interface unit 3600 may implement one or more services foraccess by the system access interface unit 3900. In someimplementations, one or more services or application programminginterfaces may be exposed to one or more external devices or systems.

The semantic interface unit 3600 may generate and transmit, send, orotherwise communicate, one or more external communications, such ase-mail messages, such as periodically, in response to one or moreevents, or both. For example, the semantic interface unit 3600 maygenerate and transmit, send, or otherwise communicate, one or moreexternal communications including a portable representation, such as aportable document format representation of one or more pinboards inaccordance with a defined schedule, period, or interval. In anotherexample, the semantic interface unit 3600 may generate and transmit,send, or otherwise communicate, one or more external communications inresponse to input data indicating an express request for acommunication. In another example, the semantic interface unit 3600 maygenerate and transmit, send, or otherwise communicate, one or moreexternal communications in response to one or more defined events, suchas the expiration of a recency of access period for a user.

Although shown as a single unit in FIG. 3 , the relational search unit3700 may be implemented in a distributed configuration, which mayinclude a primary relational search unit instance and one or moresecondary relational search unit instances.

The relational search unit 3700 may generate, maintain, operate, or acombination thereof, one or more indexes, such as one or more of anontological index, a constituent data index, a control-word index, anumeral index, or a constant index, based on the low-latency data storedin the distributed in-memory database 3300, the low-latency databaseanalysis system 3000, or both. An index may be a defined data structure,or combination of data structures, for storing tokens, terms, or stringkeys, representing a set of data from one or more defined data sourcesin a form optimized for searching. For example, an index may be acollection of index shards. In some implementations, an index may besegmented into index segments and the index segments may be sharded intoindex shards. In some implementations, an index may be partitioned intoindex partitions, the index partitions may be segmented into indexsegments and the index segments may be sharded into index shards.

Generating, or building, an index may be performed to create or populatea previously unavailable index, which may be referred to as indexing thecorresponding data, and may include regenerating, rebuilding, orreindexing to update or modify a previously available index, such as inresponse to a change in the indexed data (constituent data).

The ontological index may be an index of data (ontological data)describing the ontological structure or schema of the low-latencydatabase analysis system 3000, the low-latency data stored in thedistributed in-memory database 3300, or a combination thereof. Forexample, the ontological index may include data representing the tableand column structure of the distributed in-memory database 3300. Therelational search unit 3700 may generate, maintain, or both, theontological index by communicating with, such as requesting ontologicaldata from, the distributed in-memory ontology unit 3500, the semanticinterface unit 3600, or both. Each record in the ontological index maycorrespond to a respective ontological token, such as a token thatidentifies a column by name.

The control-word index may be an index of a defined set of control-wordtokens. A control-word token may be a character, a symbol, a word, or adefined ordered sequence of characters or symbols, that is identified inone or more grammars of the low-latency database analysis system 3000 ashaving one or more defined grammatical functions, which may becontextual. For example, the control-word index may include thecontrol-word token “sum”, which may be identified in one or moregrammars of the low-latency database analysis system 3000 as indicatingan additive aggregation. In another example, the control-word index mayinclude the control-word token “top”, which may be identified in one ormore grammars of the low-latency database analysis system 3000 asindicating a maximal value from an ordered set. In another example, thecontrol-word index may include operator tokens, such as the equalityoperator token (“=”). The constant index may be an index of constanttokens such as “100” or “true”. The numeral index may be an index ofnumber word tokens (or named numbers), such as number word tokens forthe positive integers between zero and one million, inclusive. Forexample, “one hundred and twenty eight”.

A token may be a word, phrase, character, sequence of characters,symbol, combination of symbols, or the like. A token may represent adata portion in the low-latency data stored in the low-latency datastructure. For example, the relational search unit 3700 mayautomatically generate respective tokens representing the attributes,the measures, the tables, the columns, the values, unique identifiers,tags, links, keys, or any other data portion, or combination of dataportions, or a portion thereof. The relational search unit 3700 mayclassify the tokens, which may include storing token classification datain association with the tokens. For example, a token may be classifiedas an attribute token, a measure token, a value token, or the like.

The constituent data index may be an index of the constituent datavalues stored in the low-latency database analysis system 3000, such asin the distributed in-memory database 3300. The relational search unit3700 may generate, maintain, or both, the constituent data index bycommunicating with, such as requesting data from, the distributedin-memory database 3300. For example, the relational search unit 3700may send, or otherwise communicate, a message or signal to thedistributed in-memory database 3300 indicating a request to perform anindexing data-query, the relational search unit 3700 may receiveresponse data from the distributed in-memory database 3300 in responseto the requested indexing data-query, and the relational search unit3700 may generate the constituent data index, or a portion thereof,based on the response data. For example, the constituent data index mayindex data-objects.

An index shard may be used for token searching, such as exact matchsearching, prefix match searching, substring match searching, or suffixmatch searching. Exact match searching may include identifying tokens inthe index shard that matches a defined target value. Prefix matchsearching may include identifying tokens in the index shard that includea prefix, or begin with a value, such as a character or string, thatmatches a defined target value. Substring match searching may includeidentifying tokens in the index shard that include a value, such as acharacter or string, that matches a defined target value. Suffix matchsearching may include identifying tokens in the index shard that includea suffix, or end with a value, such as a character or string, thatmatches a defined target value. In some implementations, an index shardmay include multiple distinct index data structures. For example, anindex shard may include a first index data structure optimized for exactmatch searching, prefix match searching, and suffix match searching, anda second index data structure optimized for substring match searching.Traversing, or otherwise accessing, managing, or using, an index mayinclude identifying one or more of the index shards of the index andtraversing the respective index shards. In some implementations, one ormore indexes, or index shards, may be distributed, such as replicated onmultiple relational search unit instances. For example, the ontologicalindex may be replicated on each relational search unit instance.

The relational search unit 3700 may receive a request for data from thelow-latency database analysis system 3000. For example, the relationalsearch unit 3700 may receive data expressing a usage intent indicatingthe request for data in response to input, such as user input, obtainedvia a user interface, such as a user interface generated, or partiallygenerated, by the system access interface unit 3900, which may be a userinterface operated on an external device, such as one of the clientdevices 2320, 2340 shown in FIG. 2 . In some implementations, therelational search unit 3700 may receive the data expressing the usageintent from the system access interface unit 3900 or from the semanticinterface unit 3600. For example, the relational search unit 3700 mayreceive or access the data expressing the usage intent in a request fordata message or signal.

The relational search unit 3700 may process, parse, identify semantics,tokenize, or a combination thereof, the request for data to generate aresolved-request, which may include identifying a database andvisualization agnostic ordered sequence of tokens based on the dataexpressing the usage intent. The data expressing the usage intent, orrequest for data, may include request data, such as resolved-requestdata, unresolved request data, or a combination of resolved-request dataand unresolved request data. The relational search unit 3700 mayidentify the resolved-request data. The relational search unit 3700 mayidentify the unresolved request data and may tokenize the unresolvedrequest data.

Resolved-request data may be request data identified in the dataexpressing the usage intent as resolved-request data. Eachresolved-request data portion may correspond with a respective token inthe low-latency database analysis system 3000. The data expressing theusage intent may include information identifying one or more portions ofthe request data as resolved-request data.

Unresolved request data may be request data identified in the dataexpressing the usage intent as unresolved request data, or request datafor which the data expressing the usage intent omits informationidentifying the request data as resolved-request data. Unresolvedrequest data may include text or string data, which may include acharacter, sequence of characters, symbol, combination of symbols, word,sequence of words, phrase, or the like, for which information, such astokenization binding data, identifying the text or string data asresolved-request data is absent or omitted from the request data. Thedata expressing the usage intent may include information identifying oneor more portions of the request data as unresolved request data. Thedata expressing the usage intent may omit information identifyingwhether one or more portions of the request data are resolved-requestdata. The relational search unit 3700 may identify one or more portionsof the request data for which the data expressing the usage intent omitsinformation identifying whether the one or more portions of the requestdata are resolved-request data as unresolved request data.

For example, the data expressing the usage intent may include a requeststring and one or more indications that one or more portions of therequest string are resolved-request data. One or more portions of therequest string that are not identified as resolved-request data in thedata expressing the usage intent may be identified as unresolved requestdata. For example, the data expressing the usage intent may include therequest string “example text”; the data expressing the usage intent mayinclude information indicating that the first portion of the requeststring, “example”, is resolved-request data; and the data expressing theusage intent may omit information indicating that the second portion ofthe request string, “text”, is resolved-request data.

The information identifying one or more portions of the request data asresolved-request data may include tokenization binding data indicating apreviously identified token corresponding to the respective portion ofthe request data. The tokenization binding data corresponding to arespective token may include, for example, one or more of a columnidentifier indicating a column corresponding to the respective token, adata type identifier corresponding to the respective token, a tableidentifier indicating a table corresponding to the respective token, anindication of an aggregation corresponding to the respective token, oran indication of a join path associated with the respective token. Othertokenization binding data may be used. In some implementations, the dataexpressing the usage intent may omit the tokenization binding data andmay include an identifier that identifies the tokenization binding data.

The relational search unit 3700 may implement or access one or moregrammar-specific tokenizers, such as a tokenizer for a defineddata-analytics grammar or a tokenizer for a natural-language grammar.For example, the relational search unit 3700 may implement one or moreof a formula tokenizer, a row-level-security tokenizer, a data-analyticstokenizer, or a natural language tokenizer. Other tokenizers may beused. In some implementations, the relational search unit 3700 mayimplement one or more of the grammar-specific tokenizers, or a portionthereof, by accessing another component of the low-latency databaseanalysis system 3000 that implements the respective grammar-specifictokenizer, or a portion thereof. For example, the natural languageprocessing unit 3710 may implement the natural language tokenizer andthe relational search unit 3700 may access the natural languageprocessing unit 3710 to implement natural language tokenization.

A tokenizer, such as the data-analytics tokenizer, may parse text orstring data (request string), such as string data included in a dataexpressing the usage intent, in a defined read order, such as from leftto right, such as on a character-by-character or symbol-by-symbol basis.For example, a request string may include a single character, symbol, orletter, and tokenization may include identifying one or more tokensmatching, or partially matching, the input character.

Tokenization may include parsing the request string to identify one ormore words or phrases. For example, the request string may include asequence of characters, symbols, or letters, and tokenization mayinclude parsing the sequence of characters in a defined order, such asfrom left to right, to identify distinct words or terms and identifyingone or more tokens matching the respective words. In someimplementations, word or phrase parsing may be based on one or more of aset of defined delimiters, such as a whitespace character, a punctuationcharacter, or a mathematical operator.

The relational search unit 3700 may traverse one or more of the indexesto identify one or more tokens corresponding to a character, word, orphrase identified in request string. Tokenization may includeidentifying multiple candidate tokens matching a character, word, orphrase identified in request string. Candidate tokens may be ranked orordered, such as based on probabilistic utility.

Tokenization may include match-length maximization. Match-lengthmaximization may include ranking or ordering candidate matching tokensin descending magnitude order. For example, the longest candidate token,having the largest cardinality of characters or symbols, matching therequest string, or a portion thereof, may be the highest rankedcandidate token. For example, the request string may include a sequenceof words or a semantic phrase, and tokenization may include identifyingone or more tokens matching the input semantic phrase. In anotherexample, the request string may include a sequence of phrases, andtokenization may include identifying one or more tokens matching theinput word sequence. In some implementations, tokenization may includeidentifying the highest ranked candidate token for a portion of therequest string as a resolved token for the portion of the requeststring.

The relational search unit 3700 may implement one or more finite statemachines. For example, tokenization may include using one or more finitestate machines. A finite state machine may model or represent a definedset of states and a defined set of transitions between the states. Astate may represent a condition of the system represented by the finitestate machine at a defined temporal point. A finite state machine maytransition from a state (current state) to a subsequent state inresponse to input (e.g., input to the finite state machine). Atransition may define one or more actions or operations that therelational search unit 3700 may implement. One or more of the finitestate machines may be non-deterministic, such that the finite statemachine may transition from a state to zero or more subsequent states.

The relational search unit 3700 may generate, instantiate, or operate atokenization finite state machine, which may represent the respectivetokenization grammar. Generating, instantiating, or operating a finitestate machine may include operating a finite state machine traverser fortraversing the finite state machine. Instantiating the tokenizationfinite state machine may include entering an empty state, indicating theabsence of received input. The relational search unit 3700 may initiateor execute an operation, such as an entry operation, corresponding tothe empty state in response to entering the empty state. Subsequently,the relational search unit 3700 may receive input data, and thetokenization finite state machine may transition from the empty state toa state corresponding to the received input data. In some embodiments,the relational search unit 3700 may initiate one or more data-queries inresponse to transitioning to or from a respective state of a finitestate machine. In the tokenization finite state machine, a state mayrepresent a possible next token in the request string. The tokenizationfinite state machine may transition between states based on one or moredefined transition weights, which may indicate a probability oftransiting from a state to a subsequent state.

The tokenization finite state machine may determine tokenization basedon probabilistic path utility. Probabilistic path utility may rank ororder multiple candidate traversal paths for traversing the tokenizationfinite state machine based on the request string. The candidate pathsmay be ranked or ordered based on one or more defined probabilistic pathutility metrics, which may be evaluated in a defined sequence. Forexample, the tokenization finite state machine may determineprobabilistic path utility by evaluating the weights of the respectivecandidate transition paths, the lengths of the respective candidatetransition paths, or a combination thereof. In some implementations, theweights of the respective candidate transition paths may be evaluatedwith high priority relative to the lengths of the respective candidatetransition paths.

In some implementations, one or more transition paths evaluated by thetokenization finite state machine may include a bound state such thatthe candidate tokens available for tokenization of a portion of therequest string may be limited based on the tokenization of a previouslytokenized portion of the request string.

Tokenization may include matching a portion of the request string to oneor more token types, such as a constant token type, a column name tokentype, a value token type, a control-word token type, a date value tokentype, a string value token type, or any other token type defined by thelow-latency database analysis system 3000. A constant token type may bea fixed, or invariant, token type, such as a numeric value. A columnname token type may correspond with a name of a column in the datamodel. A value token type may correspond with an indexed data value. Acontrol-word token type may correspond with a defined set ofcontrol-words. A date value token type may be similar to a control-wordtoken type and may correspond with a defined set of control-words fordescribing temporal information. A string value token type maycorrespond with an unindexed value.

Token matching may include ordering or weighting candidate token matchesbased on one or more token matching metrics. Token matching metrics mayinclude whether a candidate match is within a defined data scope, suchas a defined set of tables, wherein a candidate match outside thedefined data scope (out-of-scope) may be ordered or weighted lower thana candidate match within the define data scope (in-scope). Tokenmatching metrics may include whether, or the degree to which, acandidate match increases query complexity, such as by spanning multipleroots, wherein a candidate match that increases complexity may beordered or weighted lower than a candidate match that does not increasecomplexity or increases complexity to a lesser extent. Token matchingmetrics may include whether the candidate match is an exact match or apartial match, wherein a candidate match that is a partial may beordered or weighted lower than a candidate match that is an exact match.In some implementations, the cardinality of the set of partial matchesmay be limited to a defined value.

Token matching metrics may include a token score (TokenScore), wherein acandidate match with a relatively low token score may be ordered orweighted lower than a candidate match with a relatively high tokenscore. The token score for a candidate match may be determined based oneor more token scoring metrics. The token scoring metrics may include afinite state machine transition weight metric (FSMScore), wherein aweight of transitioning from a current state of the tokenization finitestate machine to a state indicating a candidate matching token is thefinite state machine transition weight metric. The token scoring metricsmay include a cardinality penalty metric (CardinalityScore), wherein acardinality of values (e.g., unique values) corresponding to thecandidate matching token is used as a penalty metric (inversecardinality), which may reduce the token score. The token scoringmetrics may include an index utility metric (IndexScore), wherein adefined utility value, such as one, associated with an object, such as acolumn wherein the matching token represents the column or a value fromthe column, is the index utility metric. In some implementations, thedefined utility values may be configured, such as in response to userinput, on a per object (e.g., per column) basis. The token scoringmetrics may include a usage metric (UBRScore). The usage metric may bedetermined based on a usage based ranking index, one or more usageranking metrics, or a combination thereof. Determining the usage metric(UBRScore) may include determining a usage boost value (UBRBoost). Thetoken score may be determined based on a defined combination of tokenscoring metrics. For example, determining the token score may beexpressed as the following:TokenScore=FSMScore*(IndexScore+UBRScore*UBRBoost)+Min(CardinalityScore,1).

Token matching may include grouping candidate token matches by matchtype, ranking or ordering on a per-match type basis based on tokenscore, and ranking or ordering the match types. For example, the matchtypes may include a first match type for exact matches (having thehighest match type priority order), a second match type for prefixmatches on ontological data (having a match type priority order lowerthan the first match type), a third match type for substring matches onontological data and prefix matches on data values (having a match typepriority order lower than the second match type), a fourth match typefor substring matches on data values (having a match type priority orderlower than the third match type), and a fifth match type for matchesomitted from the first through fourth match types (having a match typepriority order lower than the fourth match type). Other match types andmatch type orders may be used.

Tokenization may include ambiguity resolution. Ambiguity resolution mayinclude token ambiguity resolution, join-path ambiguity resolution, orboth. In some implementations, ambiguity resolution may ceasetokenization in response to the identification of an automatic ambiguityresolution error or failure.

Token ambiguity may correspond with identifying two or more exactlymatching candidate matching tokens. Token ambiguity resolution may bebased on one or more token ambiguity resolution metrics. The tokenambiguity resolution metrics may include using available previouslyresolved token matching or binding data and token ambiguity may beresolved in favor of available previously resolved token matching orbinding data, other relevant tokens resolved from the request string, orboth. The token ambiguity resolution may include resolving tokenambiguity in favor of integer constants. The token ambiguity resolutionmay include resolving token ambiguity in favor of control-words, such asfor tokens at the end of a request for data, such as last, that are notbeing edited.

Join-path ambiguity may correspond with identifying matching tokenshaving two or more candidate join paths. Join-path ambiguity resolutionmay be based on one or more join-path ambiguity resolution metrics. Thejoin-path ambiguity resolution metrics may include using availablepreviously resolved join-path binding data and join-path ambiguity maybe resolved in favor of available previously resolved join-paths. Thejoin-path ambiguity resolution may include favoring join paths thatinclude in-scope objects over join paths that include out-of-scopeobjects. The join-path ambiguity resolution metrics may include acomplexity minimization metric, which may favor a join path that omitsor avoids increasing complexity over join paths that increasecomplexity, such as a join path that may introduce a chasm trap.

The relational search unit 3700 may identify a resolved-request based onthe request string. The resolved-request, which may be database andvisualization agnostic, may be expressed or communicated as an orderedsequence of tokens representing the request for data indicated by therequest string. The relational search unit 3700 may instantiate, orgenerate, one or more resolved-request objects. For example, therelational search unit 3700 may create or store a resolved-requestobject corresponding to the resolved-request in the distributedin-memory ontology unit 3500. The relational search unit 3700 maytransmit, send, or otherwise make available, the resolved-request to thesemantic interface unit 3600.

In some implementations, the relational search unit 3700 may transmit,send, or otherwise make available, one or more resolved-requests, orportions thereof, to the semantic interface unit 3600 in response tofinite state machine transitions. For example, the relational searchunit 3700 may instantiate a search object in response to a firsttransition of a finite state machine. The relational search unit 3700may include a first search object instruction in the search object inresponse to a second transition of the finite state machine. Therelational search unit 3700 may send the search object including thefirst search object instruction to the semantic interface unit 3600 inresponse to the second transition of the finite state machine. Therelational search unit 3700 may include a second search objectinstruction in the search object in response to a third transition ofthe finite state machine. The relational search unit 3700 may send thesearch object including the search object instruction, or a combinationof the first search object instruction and the second search objectinstruction, to the semantic interface unit 3600 in response to thethird transition of the finite state machine. The search objectinstructions may be represented using any annotation, instruction, text,message, list, pseudo-code, comment, or the like, or any combinationthereof that may be converted, transcoded, or translated into structuredsearch instructions for retrieving data from the low-latency data.

The relational search unit 3700 may provide an interface to permit thecreation of user-defined syntax. For example, a user may associate astring with one or more tokens. Accordingly, when the string is entered,the pre-associated tokens are returned in lieu of searching for tokensto match the input.

The relational search unit 3700 may include a localization unit (notexpressly shown). The localization, globalization, regionalization, orinternationalization, unit may obtain source data expressed inaccordance with a source expressive-form and may output destination datarepresenting the source data, or a portion thereof, and expressed usinga destination expressive-form. The data expressive-forms, such as thesource expressive-form and the destination expressive-form, may includeregional or customary forms of expression, such as numeric expression,temporal expression, currency expression, alphabets, natural-languageelements, measurements, or the like. For example, the sourceexpressive-form may be expressed using a canonical-form, which mayinclude using a natural-language, which may be based on English, and thedestination expressive-form may be expressed using a locale-specificform, which may include using another natural-language, which may be anatural-language that differs from the canonical-language. In anotherexample, the destination expressive-form and the source expressive-formmay be locale-specific expressive-forms and outputting the destinationexpressive-form representation of the source expressive-form data mayinclude obtaining a canonical-form representation of the sourceexpressive-form data and obtaining the destination expressive-formrepresentation based on the canonical-form representation. Although, forsimplicity and clarity, the grammars described herein, such as thedata-analytics grammar and the natural language search grammar, aredescribed with relation to the canonical expressive-form, theimplementation of the respective grammars, or portions thereof,described herein may implement locale-specific expressive-forms. Forexample, the data-analytics tokenizer may include multiplelocale-specific data-analytics tokenizers.

The natural language processing unit 3710 may receive input dataincluding a natural language string, such as a natural language stringgenerated in accordance with user input. The natural language string mayrepresent a data request expressed in an unrestricted natural languageform, for which data identified or obtained prior to, or in conjunctionwith, receiving the natural language string by the natural languageprocessing unit 3710 indicating the semantic structure, correlation tothe low-latency database analysis system 3000, or both, for at least aportion of the natural language string is unavailable or incomplete.Although not shown separately in FIG. 3 , in some implementations, thenatural language string may be generated or determined based onprocessing an analog signal, or a digital representation thereof, suchas an audio stream or recording or a video stream or recording, whichmay include using speech-to-text conversion.

The natural language processing unit 3710 may analyze, process, orevaluate the natural language string, or a portion thereof, to generateor determine the semantic structure, correlation to the low-latencydatabase analysis system 3000, or both, for at least a portion of thenatural language string. For example, the natural language processingunit 3710 may identify one or more words or terms in the naturallanguage string and may correlate the identified words to tokens definedin the low-latency database analysis system 3000. In another example,the natural language processing unit 3710 may identify a semanticstructure for the natural language string, or a portion thereof. Inanother example, the natural language processing unit 3710 may identifya probabilistic intent for the natural language string, or a portionthereof, which may correspond to an operative feature of the low-latencydatabase analysis system 3000, such as retrieving data from the internaldata, analyzing data the internal data, or modifying the internal data.

The natural language processing unit 3710 may send, transmit, orotherwise communicate request data indicating the tokens, relationships,semantic data, probabilistic intent, or a combination thereof or one ormore portions thereof, identified based on a natural language string tothe relational search unit 3700.

The data utility unit 3720 may receive, process, and maintainuser-agnostic utility data, such as system configuration data,user-specific utility data, such as utilization data, or bothuser-agnostic and user-specific utility data. The utility data mayindicate whether a data portion, such as a column, a record, an insight,or any other data portion, has high utility or low utility within thesystem, such as among the users of the system. For example, the utilitydata may indicate that a defined column is a high-utility column or alow-utility column. The data utility unit 3720 may store the utilitydata, such as using the low-latency data structure. For example, inresponse to a user using, or accessing, a data portion, data utilityunit 3720 may store utility data indicating the usage, or access, eventfor the data portion, which may include incrementing a usage eventcounter associated with the data portion. In some embodiments, the datautility unit 3720 may receive the information indicating the usage, oraccess, event for the data portion from the insight unit 3730, and theusage, or access, event for the data portion may indicate that the usageis associated with an insight.

The data utility unit 3720 may receive a signal, message, or othercommunication, indicating a request for utility information. The requestfor utility information may indicate an object or data portion. The datautility unit 3720 may determine, identify, or obtain utility dataassociated with the identified object or data portion. The data utilityunit 3720 may generate and send utility response data responsive to therequest that may indicate the utility data associated with theidentified object or data portion.

The data utility unit 3720 may generate, maintain, operate, or acombination thereof, one or more indexes, such as one or more of a usage(or utility) index, a resolved-request index, or a phrase index, basedon the low-latency data stored in the distributed in-memory database3300, the low-latency database analysis system 3000, or both.

The insight unit 3730 may automatically identify one or more insights,which may be data other than data expressly requested by a user, andwhich may be identified and prioritized, or both, based on probabilisticutility.

The object search unit 3800 may generate, maintain, operate, or acombination thereof, one or more object-indexes, which may be based onthe analytical-objects represented in the low-latency database analysissystem 3000, or a portion thereof, such as pinboards, answers, andworksheets. An object-index may be a defined data structure, orcombination of data structures, for storing analytical-object data in aform optimized for searching. Although shown as a single unit in FIG. 3, the object search unit 3800 may interface with a distinct, separate,object indexing unit (not expressly shown).

The object search unit 3800 may include an object-index populationinterface, an object-index search interface, or both. The object-indexpopulation interface may obtain and store, load, or populateanalytical-object data, or a portion thereof, in the object-indexes. Theobject-index search interface may efficiently access or retrieveanalytical-object data from the object-indexes such as by searching ortraversing the object-indexes, or one or more portions thereof. In someimplementations, the object-index population interface, or a portionthereof, may be a distinct, independent unit.

The object-index population interface may populate, update, or both theobject-indexes, such as periodically, such as in accordance with adefined temporal period, such as thirty minutes. Populating, orupdating, the object-indexes may include obtaining object indexing datafor indexing the analytical-objects represented in the low-latencydatabase analysis system 3000. For example, the object-index populationinterface may obtain the analytical-object indexing data, such as fromthe distributed in-memory ontology unit 3500. Populating, or updating,the object-indexes may include generating or creating an indexing datastructure representing an object. The indexing data structure forrepresenting an object may differ from the data structure used forrepresenting the object in other components of the low-latency databaseanalysis system 3000, such as in the distributed in-memory ontology unit3500.

The object indexing data for an analytical-object may be a subset of theobject data for the analytical-object. The object indexing data for ananalytical-object may include an object identifier for theanalytical-object uniquely identifying the analytical-object in thelow-latency database analysis system 3000, or in a defined data-domainwithin the low-latency database analysis system 3000. The low-latencydatabase analysis system 3000 may uniquely, unambiguously, distinguishan object from other objects based on the object identifier associatedwith the object. The object indexing data for an analytical-object mayinclude data non-uniquely identifying the object. The low-latencydatabase analysis system 3000 may identify one or moreanalytical-objects based on the non-uniquely identifying data associatedwith the respective objects, or one or more portions thereof. In someimplementations, an object identifier may be an ordered combination ofnon-uniquely identifying object data that, as expressed in the orderedcombination, is uniquely identifying. The low-latency database analysissystem 3000 may enforce the uniqueness of the object identifiers.

Populating, or updating, the object-indexes may include indexing theanalytical-object by including or storing the object indexing data inthe object-indexes. For example, the object indexing data may includedata for an analytical-object, the object-indexes may omit data for theanalytical-object, and the object-index population interface may includeor store the object indexing data in an object-index. In anotherexample, the object indexing data may include data for ananalytical-object, the object-indexes may include data for theanalytical-object, and the object-index population interface may updatethe object indexing data for the analytical-object in the object-indexesin accordance with the object indexing data.

Populating, or updating, the object-indexes may include obtaining objectutility data for the analytical-objects represented in the low-latencydatabase analysis system 3000. For example, the object-index populationinterface may obtain the object utility data, such as from the objectutility unit 3810. The object-index population interface may include theobject utility data in the object-indexes in association with thecorresponding objects.

In some implementations, the object-index population interface mayreceive, obtain, or otherwise access the object utility data from adistinct, independent, object utility data population unit, which mayread, obtain, or otherwise access object utility data from the objectutility unit 3810 and may send, transmit, or otherwise provide, theobject utility data to the object search unit 3800. The object utilitydata population unit may send, transmit, or otherwise provide, theobject utility data to the object search unit 3800 periodically, such asin accordance with a defined temporal period, such as thirty minutes.

The object-index search interface may receive, access, or otherwiseobtain data expressing a usage intent with respect to the low-latencydatabase analysis system 3000, which may represent a request to accessdata in the low-latency database analysis system 3000, which mayrepresent a request to access one or more analytical-objects representedin the low-latency database analysis system 3000. The object-indexsearch interface may generate one or more object-index queries based onthe data expressing the usage intent. The object-index search interfacemay send, transmit, or otherwise make available the object-index queriesto one or more of the object-indexes.

The object-index search interface may receive, obtain, or otherwiseaccess object search results data indicating one or moreanalytical-objects identified by searching or traversing theobject-indexes in accordance with the object-index queries. Theobject-index search interface may sort or rank the object search resultsdata based on probabilistic utility in accordance with the objectutility data for the analytical-objects in the object search resultsdata. In some implementations, the object-index search interface mayinclude one or more object search ranking metrics with the object-indexqueries and may receive the object search results data sorted or rankedbased on probabilistic utility in accordance with the object utilitydata for the objects in the object search results data and in accordancewith the object search ranking metrics.

For example, the data expressing the usage intent may include a useridentifier, and the object search results data may include object searchresults data sorted or ranked based on probabilistic utility for theuser. In another example, the data expressing the usage intent mayinclude a user identifier and one or more search terms, and the objectsearch results data may include object search results data sorted orranked based on probabilistic utility for the user identified bysearching or traversing the object-indexes in accordance with the searchterms.

The object-index search interface may generate and send, transmit, orotherwise make available the sorted or ranked object search results datato another component of the low-latency database analysis system 3000,such as for further processing and display to the user.

The object utility unit 3810 may receive, process, and maintainuser-specific object utility data for objects represented in thelow-latency database analysis system 3000. The user-specific objectutility data may indicate whether an object has high utility or lowutility for the user.

The object utility unit 3810 may store the user-specific object utilitydata, such as on a per-object basis, a per-activity basis, or both. Forexample, in response to data indicating an object access activity, suchas a user using, viewing, or otherwise accessing, an object, the objectutility unit 3810 may store user-specific object utility data indicatingthe object access activity for the object, which may includeincrementing an object access activity counter associated with theobject, which may be a user-specific object access activity counter. Inanother example, in response to data indicating an object storageactivity, such as a user storing an object, the object utility unit 3810may store user-specific object utility data indicating the objectstorage activity for the object, which may include incrementing astorage activity counter associated with the object, which may be auser-specific object storage activity counter. The user-specific objectutility data may include temporal information, such as a temporallocation identifier associated with the object activity. Otherinformation associated with the object activity may be included in theobject utility data.

The object utility unit 3810 may receive a signal, message, or othercommunication, indicating a request for object utility information. Therequest for object utility information may indicate one or more objects,one or more users, one or more activities, temporal information, or acombination thereof. The request for object utility information mayindicate a request for object utility data, object utility counter data,or both.

The object utility unit 3810 may determine, identify, or obtain objectutility data in accordance with the request for object utilityinformation. The object utility unit 3810 may generate and send objectutility response data responsive to the request that may indicate theobject utility data, or a portion thereof, in accordance with therequest for object utility information.

For example, a request for object utility information may indicate auser, an object, temporal information, such as information indicating atemporal span, and an object activity, such as the object accessactivity. The request for object utility information may indicate arequest for object utility counter data. The object utility unit 3810may determine, identify, or obtain object utility counter dataassociated with the user, the object, and the object activity having atemporal location within the temporal span, and the object utility unit3810 may generate and send object utility response data including theidentified object utility counter data.

In some implementations, a request for object utility information mayindicate multiple users, or may omit indicating a user, and the objectutility unit 3810 may identify user-agnostic object utility dataaggregating the user-specific object utility data. In someimplementations, a request for object utility information may indicatemultiple objects, may omit indicating an object, or may indicate anobject type, such as answer, pinboard, or worksheet, and the objectutility unit 3810 may identify the object utility data by aggregatingthe object utility data for multiple objects in accordance with therequest. Other object utility aggregations may be used.

The system configuration unit 3820 implement or apply one or morelow-latency database analysis system configurations to enable, disable,or configure one or more operative features of the low-latency databaseanalysis system 3000. The system configuration unit 3820 may store datarepresenting or defining the one or more low-latency database analysissystem configurations. The system configuration unit 3820 may receivesignals or messages indicating input data, such as input data generatedvia a system access interface, such as a user interface, for accessingor modifying the low-latency database analysis system configurations.The system configuration unit 3820 may generate, modify, delete, orotherwise maintain the low-latency database analysis systemconfigurations, such as in response to the input data. The systemconfiguration unit 3820 may generate or determine output data, and mayoutput the output data, for a system access interface, or a portion orportions thereof, for the low-latency database analysis systemconfigurations, such as for presenting a user interface for thelow-latency database analysis system configurations. Although not shownin FIG. 3 , the system configuration unit 3820 may communicate with arepository, such as an external centralized repository, of low-latencydatabase analysis system configurations; the system configuration unit3820 may receive one or more low-latency database analysis systemconfigurations from the repository, and may control or configure one ormore operative features of the low-latency database analysis system 3000in response to receiving one or more low-latency database analysissystem configurations from the repository.

The user customization unit 3830 may receive, process, and maintainuser-specific utility data, such as user defined configuration data,user defined preference data, or a combination thereof. Theuser-specific utility data may indicate whether a data portion, such asa column, a record, autonomous-analysis data, or any other data portionor object, has high utility or low utility to an identified user. Forexample, the user-specific utility data may indicate that a definedcolumn is a high-utility column or a low-utility column. The usercustomization unit 3830 may store the user-specific utility data, suchas using the low-latency data structure. The user-specific utility datamay include, feedback data, such as feedback indicating user inputexpressly describing or representing the utility of a data portion orobject in response to utilization of the data portion or object, such aspositive feedback indicating high utility or negative feedbackindicating low utility. The user customization unit 3830 may store thefeedback in association with a user identifier. The user customizationunit 3830 may store the feedback in association with the context inwhich feedback was obtained. The user customization data, or a portionthereof, may be stored in an in-memory storage unit of the low-latencydatabase analysis system. In some implementations, the usercustomization data, or a portion thereof, may be stored in thepersistent storage unit 3930.

The system access interface unit 3900 may interface with, or communicatewith, a system access unit (not shown in FIG. 3 ), which may be a clientdevice, a user device, or another external device or system, or acombination thereof, to provide access to the internal data, features ofthe low-latency database analysis system 3000, or a combination thereof.For example, the system access interface unit 3900 may receive signals,message, or other communications representing interactions with theinternal data, such as data expressing a usage intent and may outputresponse messages, signals, or other communications responsive to thereceived requests.

The system access interface unit 3900 may generate data for presenting auser interface, or one or more portions thereof, for the low-latencydatabase analysis system 3000. For example, the system access interfaceunit 3900 may generate instructions for rendering, or otherwisepresenting, the user interface, or one or more portions thereof and maytransmit, or otherwise make available, the instructions for rendering,or otherwise presenting, the user interface, or one or more portionsthereof to the system access unit, for presentation to a user of thesystem access unit. For example, the system access unit may present theuser interface via a web browser or a web application and theinstructions may be in the form of HTML, JavaScript, or the like.

In an example, the system access interface unit 3900 may include adata-analytics field user interface element in the user interface. Thedata-analytics field user interface element may be an unstructuredstring user input element or field. The system access unit may displaythe unstructured string user input element. The system access unit mayreceive input data, such as user input data, corresponding to theunstructured string user input element. The system access unit maytransmit, or otherwise make available, the unstructured string userinput to the system access interface unit 3900. The user interface mayinclude other user interface elements and the system access unit maytransmit, or otherwise make available, other user input data to thesystem access interface unit 3900.

The system access interface unit 3900 may obtain the user input data,such as the unstructured string, from the system access unit. The systemaccess interface unit 3900 may transmit, or otherwise make available,the user input data to one or more of the other components of thelow-latency database analysis system 3000.

In some embodiments, the system access interface unit 3900 may obtainthe unstructured string user input as a sequence of individualcharacters or symbols, and the system access interface unit 3900 maysequentially transmit, or otherwise make available, individual or groupsof characters or symbols of the user input data to one or more of theother components of the low-latency database analysis system 3000.

In some embodiments, system access interface unit 3900 may obtain theunstructured string user input may as a sequence of individualcharacters or symbols, the system access interface unit 3900 mayaggregate the sequence of individual characters or symbols, and maysequentially transmit, or otherwise make available, a currentaggregation of the received user input data to one or more of the othercomponents of the low-latency database analysis system 3000, in responseto receiving respective characters or symbols from the sequence, such ason a per-character or per-symbol basis.

The real-time collaboration unit 3910 may receive signals or messagesrepresenting input received in accordance with multiple users, ormultiple system access devices, associated with a collaboration contextor session, may output data, such as visualizations, generated ordetermined by the low-latency database analysis system 3000 to multipleusers associated with the collaboration context or session, or both. Thereal-time collaboration unit 3910 may receive signals or messagesrepresenting input received in accordance with one or more usersindicating a request to establish a collaboration context or session,and may generate, maintain, or modify collaboration data representingthe collaboration context or session, such as a collaboration sessionidentifier. The real-time collaboration unit 3910 may receive signals ormessages representing input received in accordance with one or moreusers indicating a request to participate in, or otherwise associatewith, a currently active collaboration context or session, and mayassociate the one or more users with the currently active collaborationcontext or session. In some implementations, the input, output, or both,of the real-time collaboration unit 3910 may include synchronizationdata, such as temporal data, that may be used to maintainsynchronization, with respect to the collaboration context or session,among the low-latency database analysis system 3000 and one or moresystem access devices associated with, or otherwise accessing, thecollaboration context or session.

The third-party integration unit 3920 may include an electroniccommunication interface, such as an application programming interface(API), for interfacing or communicating between an external, such asthird-party, application or system, and the low-latency databaseanalysis system 3000. For example, the third-party integration unit 3920may include an electronic communication interface to transfer databetween the low-latency database analysis system 3000 and one or moreexternal applications or systems, such as by importing data into thelow-latency database analysis system 3000 from the external applicationsor systems or exporting data from the low-latency database analysissystem 3000 to the external applications or systems. For example, thethird-party integration unit 3920 may include an electroniccommunication interface for electronic communication with an externalexchange, transfer, load (ETL) system, which may import data into thelow-latency database analysis system 3000 from an external data sourceor may export data from the low-latency database analysis system 3000 toan external data repository. In another example, the third-partyintegration unit 3920 may include an electronic communication interfacefor electronic communication with external machine learning analysissoftware, which may export data from the low-latency database analysissystem 3000 to the external machine learning analysis software and mayimport data into the low-latency database analysis system 3000 from theexternal machine learning analysis software. The third-party integrationunit 3920 may transfer data independent of, or in conjunction with, thesystem access interface unit 3900, the enterprise data interface unit3400, or both.

The persistent storage unit 3930 may include an interface for storingdata on, accessing data from, or both, one or more persistent datastorage devices or systems. For example, the persistent storage unit3930 may include one or more persistent data storage devices, such asthe static memory 1200 shown in FIG. 1 . Although shown as a single unitin FIG. 3 , the persistent storage unit 3930 may include multiplecomponents, such as in a distributed or clustered configuration. Thepersistent storage unit 3930 may include one or more internalinterfaces, such as electronic communication or application programminginterfaces, for receiving data from, sending data to, or both othercomponents of the low-latency database analysis system 3000. Thepersistent storage unit 3930 may include one or more externalinterfaces, such as electronic communication or application programminginterfaces, for receiving data from, sending data to, or both, one ormore external systems or devices, such as an external persistent storagesystem. For example, the persistent storage unit 3930 may include aninternal interface for obtaining key-value tuple data from othercomponents of the low-latency database analysis system 3000, an externalinterface for sending the key-value tuple data to, or storing thekey-value tuple data on, an external persistent storage system, anexternal interface for obtaining, or otherwise accessing, the key-valuetuple data from the external persistent storage system, and an internalkey-value tuple data for sending, or otherwise making available, thekey-value tuple data to other components of the low-latency databaseanalysis system 3000. In another example, the persistent storage unit3930 may include a first external interface for storing data on, orobtaining data from, a first external persistent storage system, and asecond external interface for storing data on, or obtaining data from, asecond external persistent storage system.

FIG. 4 is a flowchart of an example of a technique 4000 for validatingcompacted replay logs. The technique 4000 can be implemented, forexample, as a software program that may be executed by a computingdevice, such as the computing device 1000 of FIG. 1 . The softwareprogram can include machine-readable instructions that may be stored ina memory such as the static memory 1200, the low-latency memory 1300, orboth of FIG. 1 , and that, when executed by a processor, such theprocessor 1100 of FIG. 1 , may cause the computing device to perform thetechnique 4000. The technique 4000 may be implemented by a databasesystem, such as the low-latency database analysis system 3000 shown inFIG. 3 . The technique 4000 may be implemented in whole or in part byone or more units of the database system that may perform replay logcompaction, replay log validation, storage management, backup, dataloading, database management, data restoration, some other function ofthe database system, or a combination thereof. In an example, at leastone of the enterprise data interface unit 3400 or the distributedcluster manager 3100 of FIG. 3 may implement the technique 4000. Thetechnique 4000 can be implemented using specialized hardware orfirmware. Multiple processors, memories, or both, may be used. Thedistributed database can include a first database instance and a seconddatabase instance.

Compacting replay logs results in a compacted replay log. The technique4000 validates that the replay logs and the compacted replay logresulting therefrom are or are likely to be equivalent in the sense thatthe replay logs and the compacted replay log result, when replayed,result in the same, or likely the same, data (i.e., the same rows andcolumn values). As, depending on the hashing algorithm used, twodifferent strings may generate the same hash value, there may raresituations where two different resulting rows may have the same hashvalue and may not be reported (e.g., flagged, identified, etc.) as anon-match. As is known, with some hashing functions, collisions are lesslikely. The technique 4000 may be performed in response to, andsubsequent to, the database system compacting the replay logs. Inanother example, the technique 4000 may be performed in response to thedatabase system receiving or issuing a command to delete the replaylogs.

At 4100, the technique 4000 identifies replay logs to be compacted. Thereplay logs may be identified in response to a determination that atable is eligible for compaction. In an example, the replay logs may beidentified in response to a command (such as of an administrator) tocompact data of a table. Other ways of identifying the first replay logsare possible.

In an example, the replay logs may include database manipulationcommands for one table of the database. In an example, the replay logsmay include database manipulation commands for more than one table ofthe database. In an example, the replay logs may be or may result fromone or more loading events. The replay logs may constitute a versionchain of replay logs. In an example, at least one of the replay logs maybe or may correspond to a snapshot of low-latency data of one or moretables of the in-memory database. As described above, a snapshot may bestored in the format of a replay log that includes only INSERT commands.As such, the at least one of the replay logs may be a compacted replaylog that is the result of a previous successful compaction.

At 4200, the technique 4000 obtains the compacted replay log from thereplay logs. As mentioned above, the compacted replay log can includeonly INSERT commands. INSERT commands can be used to insert new records(e.g., rows) in a table of the database. The INSERT commands of thecompacted replay log constitute the non-obsolete, non-redundant, orotherwise non-supplanted commands of the set of commands of the replaylogs. Any known technique for obtaining the compacted replay log fromthe replay logs can be used.

FIG. 5 illustrates an example 5000 of replay logs and a compacted replaylog resulting therefrom. The example 5000 includes replay logs 5100corresponding to four loading events, as illustrated by a loading eventindicator 5110. For ease of reference, all the commands of the fourreplay logs are numbered sequentially, in the order they are processed(e.g., executed, performed, etc.), and as indicated by a command numberindicator 5120. For example, even though a command 5140 is the onlycommand of the second loading event, the command 5140 is numbered three(3) to indicate that it is processed later in time than the commandsnumbered 2 and 1 of the first loading event.

The commands of the example 5000 perform database manipulation commandson the Animals table, which is described above as including the columnsID, NAME, and SOUND. The columns id, name, and sound are illustrated ashaving the data types integer, string, and string, respectively.However, the disclosure herein is not so limited and a table can includeany number of columns that can be of any data types. As the semantics ofeach of the commands of the replay logs 5100 are understood by a personskilled in the art, descriptions thereof are omitted for brevity.

The example 5000 illustrates a compacted replay log 5200 that resultsfrom compacting the replay logs 5100. That is, processing the commands1-7 of the replay logs 5100, in that order, may be (barring any errors,data corruption, etc.) equivalent to performing only the commands of thecompacted replay log 5200. In the replay logs 5100, command number 1,when performed, inserts (i.e., adds) a first row in the Animals tablewhere ID=1, NAME=“CAT,” AND SOUND=“MEOW;” command number 2, whenperformed, inserts a second row in the Animals table where ID=2,NAME=“DOG,” and SOUND=“BARK;” command number 3, when performed, insertsa third row in the Animals table where ID=1, NAME=“HORSE,” andSOUND=“NEIGH;” command number 4, when performed, deletes any rows in theAnimals table where ID=2, which results in the second row being deleted;command number 5, when performed, updates the first row (i.e., whereID=1) to set id to 4, NAME to “COW,” and SOUND to “MOO;” command number6, when performed, updates all rows where NAME matches the pattern “HOR%” (where % is wildcard indicating: any number of characters) to set theNAME to “LION,” which results in the third row being updatedaccordingly; and command number 7, when performed, updates all rowswhere NAME is equal to “LION” to set the ID to the value 5, whichresults in the third row being updated accordingly. The replay logs 5100includes only two insert commands and result in two rows being insertedin the Animals table.

Referring to FIG. 4 again, to validate the compacted replay log, thetechnique 4000 replays, at 4300, the replay logs to obtain a firstreplay result; replays, at 4400, the compacted replay logs to obtain asecond replay result; and compares, at 4500, the first replay result tothe second replay result. Replaying, at 4300, the replay logs to obtainthe first replay result includes performing blocks 4310-4370. An exampleof the first replay result and the second replay result are describedwith respect to FIG. 6 .

At 4310, the technique 4000 identifies condition columns in the replaylogs. The technique 4000 can evaluate (e.g., visit, consider, test,etc.) each command of the replay logs to identify conditions columns. Acommand can include 0 or more condition columns. A column, as used, canmean a column name, a column reference, or any indicator of a column ofa table. A condition column of a command is a column (e.g., a columnname, a column reference, etc.) that is used in a conditional, a test,or an expression that evaluates to true or false, or is a primary keycolumn of the table. As is known, the primary key or a table may includeor may be more than one column of the table. In an example, conditioncolumns can be identified in DELETE commands, UPDATE commands, or datadefinition commands (e.g., SCHEMA_UPDATE commands), or other commandsthat may include condition columns. As primary key values can be used toquickly identify rows of the table, primary key columns are included in,and considered to be, condition columns.

It is noted that if the replay logs include data definition commands,such as a schema update command to add or delete a column to a table,the result of the command would be reflected in (e.g., performed on,applied to, etc.) the in-memory database (e.g., the low-latency data).To illustrate, assume that a table T1 includes the column C1, and afirst replay log, L1, includes the two commands “Insert into T1 the row(10)” and “Insert into T1 the row (12),” and that a second replay log,L2, includes the schema update command “Add column C2 to T1 with adefault value of 5.” Thus, the table T1 now includes the rows (10, 5)and (12, 5). As such, when the data of table T1 is exported to obtainthe compacted replay log, the compacted replay log would include theinsert commands “Insert into T1 the row (C1=10, C2=5)” and “Insert intoT1 the row (C1=12, C2=5).”

Referring again to FIG. 5 , condition columns 5150 illustrates theidentified condition columns of the replay logs 5100. No conditioncolumns are identified with respect to the commands 1, 2, or 3. Commandnumber 4 includes the condition ID=2, as such the ID column isidentified as a condition column. Command number 5 includes thecondition ID=1, as such the ID column may be again identified as acondition column. Command 6 includes the condition NAME like “HOR %”, assuch the NAME column is a condition column. Command number 7 includesthe condition NAME=“LION”, as such the NAME column is again identifiedas a condition column. Thus, the columns ID and NAME is the set ofcolumns identified as condition columns.

Referring again to FIG. 4 , the technique 4000 now performs blocks4330-4370 for each command of the replay logs to obtain the first replayresult, which consists or data (i.e., rows), or representations of data,that result from performing the commands. To illustrate, the technique4000 iteratively performs the commands 1-7 of the replay logs 5100. Inan example, the first replay result can be stored in a file, in a memorymapped file, in permanent storage, in volatile memory, in the in-memorydatabase, or some other location. Performing a command of a replay logmay result in a modification to the first replay result. For example, arow may be added to the first replay result, one or more rows may bedelete from the first replay result, one or more column values of one ormore rows of the first replay result may be updated, and so on.

At 4320, the technique 4000 determines whether there are more commandsin the replay logs to perform. If there are more commands to perform,the technique 4000 proceeds to 4330 (not shown); otherwise, thetechnique 4000 proceeds to 4400.

At 4330, the technique 4000 determines whether the command is an INSERTcommand. If the command is an INSERT command, then the technique 4000prepares a new row to be inserted in the first replay result andproceeds to 4340; otherwise, the technique 4000 proceeds to 4370. At4340, the technique 4000 stores condition columns of the INSERT command,if any, without any modifications. Stated another way, the column valuesof the INSERT command corresponding to condition columns identified at4310 are stored as is (i.e., without modification) in a new row of thefirst replay result. The values are stored as is because the originalvalues (i.e., unmodified values) will be required when the commandsusing the condition columns are performed.

At 4350, values of columns that are not condition columns and which areof type string may be stored as modified values in the row to be addedto the row to be inserted in the first replay result. The purpose ofusing the modified value is to store in the row to be inserted a valuethat requires less storage capacity than the original value but wherethe modified value is strongly correlated or strongly indicative of theoriginal value. In an example, the modified value can be a hash of theoriginal value. In an example, the original string value can beconverted to an MD5 value of a predefined size (e.g., 8 bytes, 16 bytes,etc.) for storage in the row to be inserted. As is known, MD5 is amessage-digest algorithm that takes as input a message of arbitrarylength and produces as output a 128-bit fingerprint or message digest ofthe input. Other hashing functions or other functions for obtaining themodified value are possible. In an example, the technique 4000determines whether the column type is such that the modified value(e.g., the hash value) would require more storage than the originalvalue. In such a case, the column value can be stored as is. Toillustrate, a column STATE may be configured to store a maximum of twocharacters, which requires two bytes to store (i.e., one byte percharacter). On the other hand, an 8-byte MD5 hash of any value of theSTATE column would require more than two bytes. As such, the STATE valuecan be stored as is.

At 4360, the technique 4000 stores any column values of columns that areof a type other than string as is (i.e., without modification). Whilenot specifically shown in FIG. 4 , the technique 4000 can write (e.g.append, etc.) the row to be inserted in to the first replay result.

According to the foregoing, replaying the first replay logs (i.e.,replaying first database manipulation commands of the first replay logs)can include identifying condition columns of the table; responsive tothe condition columns not including a column, obtaining a rowcorresponding to the insert command, where the row includes a modifiedvalue of the corresponding value of the column; and adding the row tothe first replay result. Condition columns are columns that are used inat least one condition of the first database manipulation commands.

From 4360, the technique 4000 proceeds back to 4320. At 4370, thetechnique 4000 performs the command, which is not an insert command. Asthe values of condition columns of the command, if any, are stored asis, the technique 4000 can unambiguously determine which rows of thefirst replay result to apply the command to.

FIG. 6 illustrates a first replay result 6100 and a second replay result6200. The first replay result 6100 illustrates the result of replaying,as described with respect to FIG. 4 , the commands of the replay logs5100. The second replay result 6200 illustrates the result of replaying,as described with respect to FIG. 4 , the commands of the compactedreplay log 5200.

The first replay result 6100 illustrates, the running result 6120 ofreplaying the command indicated by commands 6110. To illustrate, afterreplaying the command number 1, the first replay result 6100 includesthe row ‘1, “CAT”, MD5(“MEOW”)’. As the NAME column is a string columnand was identified as a condition column at 4310 of FIG. 4 , the value‘CAT’ is stored unmodified at 4340 of FIG. 4 ; as the SOUND column is astring column but is not a condition column, a modified value (e.g.,MD5(“MEOW”)) of the value of the SOUND is stored at 4350; and the valueof the ID column is stored as is at 4360 of FIG. 4 . The sameexplanation applies with respect to replaying the commands numbered 2and 3.

As used herein, MD5(<string>) should be understood to be or refer to aresult of invoking a function (i.e., an MD5( ) function or the like)with the <string> as a parameter. As such, and to illustrate, the row‘1, “CAT”, MD5(“MEOW”)’ should in fact be understood to be ‘1, “CAT”,“035466283a3b0c16f87041e2cf843f08”’, where the string“035466283a3b0c16f87041e2cf843f08” is the MD5 of the string “MEOW.”However, for ease of understanding, instead of showing actual hashvalues (e.g., “035466283a3b0c16f87041e2cf843f08”) in the figures, howthe hash values are obtained (e.g., MD5(“MEOW”)) are shown.

After command number 3 is replayed, the first replay result 6100includes the three rows indicated by rows 6130. Command number 4 deletesany rows where the value of ID is equal to 2. Thus, after replaying thecommand number 4, the first replay result 6100 includes the two rowsindicated by rows 6140. Command number 5 essentially replaces the rowwhere ID=1 with the row (4, “COW”, MD5(“MOO”)). When command number 7 isfinally replayed, the first replay result 6100 includes only the rowsindicated by rows 6150.

In an implementation, after all commands of the first replay log arereplayed, the technique 4000 can hash each of the obtained rows tofurther reduce the memory required to store the first replay results. Aresult 6160 illustrates the first replay result 6100 where each row ishashed. To obtain a hash of a row, each non-string value can beconverted into a respective string, the values are then concatenatedaccording to an ordering of the columns, and a hash of the concatenatedstring is obtained. A hashed row 6162 illustrates obtaining a hash of arow 6164 (i.e., the row ‘4, “COW”, MD5(“MOO”)’). The value of thenon-string ID column (e.g., the value 4) is converted into a string (asillustrated by the function STRING(4), which provides the string “4” ofthe integer value 4); the plus (i.e., +) operation illustrates thestring concatenation operation. As such, the strings “4,” “COW,” andMD5(“MOO”) are concatenated to obtain the string“4COWae176556a77ece20119ee093f1c0ebbb,” where“ae176556a77ece20119ee093f1c0ebbb” is the MD5 hash of “MOO.” The wholeconcatenated string is then hashed to obtain the hashed row value.

In an example, a separator can be inserted between the values of the rowbefore hashing. Using “_” as a separator, the hash value can be obtainedas MD5(STRING(4)+“_”+“COW”+“_”+MD5(“MOO”)). Other separators arepossible. In another example, each string that is not already hashed canbe hashed, the hashed strings can be concatenated, and a hashed row canbe obtained for the concatenated string. To illustrate, the hashed rowcan be obtained as MD5(MD5(STRING(4)+MD5(“COW”)+MD5(“MOO)). Other waysof obtaining the hashed row value are possible.

Referring to FIG. 4 again, at 4400, the technique 4000 replays thecompacted replay log to obtain a second replay result. As such, thetechnique 4000 replays the compacted replay log 5200 of FIG. 5 to obtainthe second replay result 6200 of FIG. 6 . The technique 4000 hashes thevalues and the rows of the second replay result 6200 in such way thatthe column values and rows are hashed similarly to the rows of the firstreplay result 6100; otherwise, the rows of the first replay result 6100and the second replay result 6200 could not be compared. It is notedthat the order of the rows in the first replay result matches the orderof the rows in the second replay result. At least in the case that thereare no errors, the second replay results would have been created in theorder specified in (i.e., by applying the commands of) the first replaylogs in the order specified in the first replay logs. That is, forexample, row number N of the first replay result corresponds to (i.e.,should be the same as, etc.) row number N of the second replay result.In an example, the technique 4000 hashes the values of only thosecolumns identified as condition columns at 4310. A result 6210illustrates hashing the rows of the second replay result 6200 similarlyto the hashing of the first replay result 6100 to obtain the result6160. In an example, the value of each string column is hashed, asdescribed with respect to the first replay result 6100. In an example,the technique 4000 uses separators between the column values (whetherhashed or not, as the case may be).

At 4500, the technique 4000 compares the first replay result to thesecond replay result. In an example, the technique 4000 compares eachhashed row value of the first replay result to a corresponding hashedrow value of the second replay result. The technique 4000 can write therow numbers of non-matching rows, such as to a log file.

In an example, in response to a first identified non-match, thetechnique 4000 can send a notification of the non-match, such as to anadministrator (e.g., a database administrator) and stop theverification. As such, in an example, the technique 4000 may not replayall of the commands of the compacted replay log 5200 to obtain thesecond replay result 6200 before commencing the comparing at 4500.Rather, the technique 4000 can perform one command of the compactedreplay log 5200 to obtain one corresponding hashed row of the secondreplay result 6200. The technique 4000 can then compare the row of thesecond replay result 6200 (i.e., the hashed row value of the row of thesecond replay result 6200) to the corresponding row of first replayresult 6100 (i.e., the hashed row value of the corresponding row offirst replay result 6100). In case of a match, the technique 4000discards the row of the second replay result 6200 and proceeds toperform a next command of the compacted replay log 5200 to obtain a nextrow of the second replay result 6200 for comparison with correspondingrow of first replay result 6100; and so on. In case of a non-match, thetechnique 4000 can stop the verification and transmit a notification ofthe non-match. As such, the technique 4000 does not consume memoryresources to hold the second replay result 6200 as the technique 4000only generates one row of the second replay result 6200 as a time.

FIG. 7 is a flowchart of an example of a technique 7000 for databasereplay log verification. The technique 700 determines whether a firstreplay log of first commands for obtaining a first in-memory database,and a second replay log of second commands for obtaining a secondin-memory database are equivalent. That the first replay log and thesecond replay log are equivalent can mean that the first commands andthe second commands, when replayed (e.g., executed, performed, etc.) areor are likely to produce the same in-memory database (i.e., the samedata). The first replay log may include one or more first replay logs.The first replay log can be as described above. In an example, thesecond replay log can be a compacted version of the first replay logthat has been generated using a compaction process, as described above.However, that need not be the case, the technique 7000 can be used tocompare any two sets of replay logs.

The technique 7000 can be implemented, for example, as a softwareprogram that may be executed by a computing device, such as thecomputing device 1000 of FIG. 1 . The software program can includemachine-readable instructions that may be stored in a memory such as thestatic memory 1200, the low-latency memory 1300, or both of FIG. 1 , andthat, when executed by a processor, such the processor 1100 of FIG. 1 ,may cause the computing device to perform the technique 7000. Thetechnique 7000 may be implemented by a database system, such as thelow-latency database analysis system 3000 shown in FIG. 3 . Thetechnique 7000 may be implemented in whole or in part by one or moreunits of the database system that may perform replay log compaction,replay log validation, storage management, backup, data loading,database management, data restoration, some other function of thedatabase system, or a combination thereof. In an example, at least oneof the enterprise data interface unit 3400 or the distributed clustermanager 3100 of FIG. 3 may implement the technique 7000. The technique7000 can be implemented using specialized hardware or firmware. Multipleprocessors, memories, or both, may be used. The distributed database caninclude a first database instance and a second database instance.

At 7100, the technique 7000 replays the first replay log to generate afirst replay result. As described above, the first replay log includescommands for obtaining an in-memory database. The first replay log canbe replayed as described with respect to FIG. 4 . As described above,replaying the first replay log can include replacing 7150 a first valueof a first field (e.g., column, etc.) included in a first command in thefirst replay log with a first hash value responsive to a determinationthat the first field is not utilized as a condition in at least onecommand included in the first replay log.

At 7200, the technique 7000 replays a second replay log to generate asecond replay result. Replaying the second replay log can includereplacing a value of a field included in a command of the second replaylog with a hash value. In a case that the replay log is a compactedversion of the first replay log that includes only INSERT commands,replaying the second replay log can be as described with respect toreplaying the second replay log of FIG. 3 . In an example, the secondreplay log may not be a compacted version of the first replay log. Assuch, the second replay log may include commands other than (e.g., inaddition to, etc.) INSERT commands and replaying the second replay logcan be as described with respect to replaying the first replay log.

In an example and in a case where the second replay log is a compactedversion of the first replay log, replacing the second value of thesecond field included in the second command of the second replay logwith the second hash value can include replacing the second value of thesecond field included in the second command of the second replay logwith the second hash value responsive to a determination that the secondfield is not utilized as a condition in commands included in the firstreplay log. In an example, and in a case where the second replay logincludes commands other than INSERT commands, replacing the second valueof the second field included in the second command of the second replaylog with the second hash value can include replacing the second value ofthe second field included in the second command of the second replay logwith the second hash value responsive to a determination that the secondfield is not utilized as a condition in commands included in the secondreplay log.

At 7300, the technique 7000 compares the first replay result and thesecond replay result to verify that the first replay log and the secondreplay log are equivalent. Comparing the first replay result and thesecond replay result to verify the compaction of the second replay logcan include comparing a third hash value of a row of the first replayresult (i.e., a hashed value of the entire row of the first replayresult) to a fourth hash value of a corresponding row of the secondreplay result (i.e., a hashed value of the entire row of the firstreplay result). Obtaining the hashed value of a row can be as describedwith respect to FIG. 4 .

Generating the second replay result at 7200 encompasses generating allthe rows of the second replay result before the comparing at 7300 andencompasses generating one row of the second replay result at a time andusing the row in the comparing at 7300 before generating a next row. Inan example, responsive to a non-match between the third hash value andthe fourth hash value, the technique 7000 transmits a notification ofthe non-match and stops the verification. In an example, transmittingthe notification of the non-match can include logging the non-match to alog file.

FIG. 8 is a flowchart of an example of a technique 8000 for databasereplay log compaction verification. The technique 8000 can beimplemented, for example, as a software program that may be executed bya computing device, such as the computing device 1000 of FIG. 1 . Thesoftware program can include machine-readable instructions that may bestored in a memory such as the static memory 1200, the low-latencymemory 1300, or both of FIG. 1 , and that, when executed by a processor,such the processor 1100 of FIG. 1 , may cause the computing device toperform the technique 8000. The technique 8000 may be implemented by adatabase system, such as the low-latency database analysis system 3000shown in FIG. 3 . The technique 8000 may be implemented in whole or inpart by one or more units of the database system that may perform replaylog compaction, replay log validation, storage management, backup, dataloading, database management, data restoration, some other function ofthe database system, or a combination thereof. In an example, at leastone of the enterprise data interface unit 3400 or the distributedcluster manager 3100 of FIG. 3 may implement the technique 8000. Thetechnique 8000 can be implemented using specialized hardware orfirmware. Multiple processors, memories, or both, may be used. Thedistributed database can include a first database instance and a seconddatabase instance.

At 8100, the technique 8000 replays, to obtain a first replay result,first database manipulation commands of at least one replay log, wherethe first database manipulation commands comprises at least one of anUPDATE command or a DELETE command. At 8200, the technique 8000 replays,to obtain a second replay result, second database manipulation commandsof a compacted replay log, where the compacted replay log includes onlyINSERT commands. The compacted replay log can be a compacted version ofthe first replay log that has been generated using a compaction process.At 8300, responsive to a row of the first replay result not matching acorresponding row of the second replay result, the technique 8000 sendsa notification indicating a non-match corresponding to the row of thefirst replay result.

In an example, responsive to the first replay result matching the secondreplay result, the technique 8000 deletes the at least one replay log.In an example, and as described above, replaying, to obtain a firstreplay result, first database manipulation commands of at least onereplay log can include identifying condition columns of the firstdatabase manipulation commands; and replacing a first value of a fieldincluded in a first command in the first database manipulation commandswith a hash value responsive to a determination that the field is notincluded in the condition columns. In an example, the technique 8000 caninclude obtaining a first hashed row value for the row of the firstreplay result and obtaining a second hashed row value for correspondingrow of the second replay result. Obtaining the first hashed row valueand the second hashed row value can be as described above. In anexample, the technique 8000 compares the first hashed row value and thesecond hashed row value to determine whether the row of the first replayresult matches the corresponding row of the second replay result.

As mentioned, validating a compacted replay log of a table can mean,encompass, or include validating a compacted replay log of a shard ofthe table. In a distributed database, such as a distributed in-memorydatabase as described herein, a table may be partitioned into shards.The data of the sharded table can be low-latency data as describedherein. Sharding a table includes distributing the data (e.g., rows) ofthe sharded table amongst the shards in such a way that a row of thesharded table is included in a shard and is omitted from the othershards. Sharding a table may include distributing the rows of the tableamongst the shards according to sharding criteria. Sharding a table mayinclude distributing the rows of the table to respective shards based onthe value in the row for a column identified by the sharding criteria.For examples, rows of a sharded table having a first value for thecolumn identified by the sharding criteria may be included in a firstshard and omitted from a second shard and rows of the sharded tablehaving a second value for the column may be included in the second shardand omitted from the first shard.

The sharding criteria can be derived from one or more columns of thetable. The sharding criteria can be derived from one column of thesharded table, can use more than one column of the table, or can be someother criteria. The sharding criteria may be used to distribute rows ofthe table amongst the available shards. This may include arranging thedistribution of the rows in a manner such that rows with the samesharding criteria value(s) are placed in the same shard where feasiblebased on the number of shards and the variation in the number of rowsper shard that is desired. In some implementations, all the rows of thetable that have the same sharding criteria value(s) may be stored inonly one of the shards. A shard can include more than one value of thesharding criteria.

A table can be sharded into tens, hundreds, or more shards. A shard caninclude, for example, zero rows or millions of rows of data. The shardscan be distributed to database instances of the distributed database,such as the in-memory database instances described herein. In anexample, the number of shards can be a multiple of the number ofdatabase instances. As such, more than one shard can be distributed to adatabase instance. The database instances may be implemented on variousdifferent computing devices. Some database instances may be implementedon the same computing device.

As used herein, the terminology “computer” or “computing device”includes any unit, or combination of units, capable of performing anymethod, or any portion or portions thereof, disclosed herein.

As used herein, the terminology “processor” indicates one or moreprocessors, such as one or more special purpose processors, one or moredigital signal processors, one or more microprocessors, one or morecontrollers, one or more microcontrollers, one or more applicationprocessors, one or more central processing units (CPU)s, one or moregraphics processing units (GPU)s, one or more digital signal processors(DSP)s, one or more application specific integrated circuits (ASIC)s,one or more application specific standard products, one or more fieldprogrammable gate arrays, any other type or combination of integratedcircuits, one or more state machines, or any combination thereof.

As used herein, the terminology “memory” indicates any computer-usableor computer-readable medium or device that can tangibly contain, store,communicate, or transport any signal or information that may be used byor in connection with any processor. For example, a memory may be one ormore read only memories (ROM), one or more random access memories (RAM),one or more registers, low power double data rate (LPDDR) memories, oneor more cache memories, one or more semiconductor memory devices, one ormore magnetic media, one or more optical media, one or moremagneto-optical media, or any combination thereof.

As used herein, the terminology “instructions” may include directions orexpressions for performing any method, or any portion or portionsthereof, disclosed herein, and may be realized in hardware, software, orany combination thereof. For example, instructions may be implemented asinformation, such as a computer program, stored in memory that may beexecuted by a processor to perform any of the respective methods,algorithms, aspects, or combinations thereof, as described herein.Instructions, or a portion thereof, may be implemented as a specialpurpose processor, or circuitry, that may include specialized hardwarefor carrying out any of the methods, algorithms, aspects, orcombinations thereof, as described herein. In some implementations,portions of the instructions may be distributed across multipleprocessors on a single device, on multiple devices, which maycommunicate directly or across a network such as a local area network, awide area network, the Internet, or a combination thereof.

As used herein, the terminology “determine,” “identify,” “obtain,” and“form” or any variations thereof, includes selecting, ascertaining,computing, looking up, receiving, determining, establishing, obtaining,or otherwise identifying or determining in any manner whatsoever usingone or more of the devices and methods shown and described herein.

As used herein, the term “computing device” includes any unit, orcombination of units, capable of performing any method, or any portionor portions thereof, disclosed herein.

As used herein, the terminology “example,” “embodiment,”“implementation,” “aspect,” “feature,” or “element” indicates serving asan example, instance, or illustration. Unless expressly indicated, anyexample, embodiment, implementation, aspect, feature, or element isindependent of each other example, embodiment, implementation, aspect,feature, or element and may be used in combination with any otherexample, embodiment, implementation, aspect, feature, or element.

As used herein, the terminology “or” is intended to mean an inclusive“or” rather than an exclusive “or.” That is, unless specified otherwise,or clear from context, “X includes A or B” is intended to indicate anyof the natural inclusive permutations. That is, if X includes A; Xincludes B; or X includes both A and B, then “X includes A or B” issatisfied under any of the foregoing instances. In addition, thearticles “a” and “an” as used in this application and the appendedclaims should generally be construed to mean “one or more” unlessspecified otherwise or clear from the context to be directed to asingular form.

Further, for simplicity of explanation, although the figures anddescriptions herein may include sequences or series of steps or stages,elements of the methods disclosed herein may occur in various orders orconcurrently. Additionally, elements of the methods disclosed herein mayoccur with other elements not explicitly presented and described herein.Furthermore, not all elements of the methods described herein may berequired to implement a method in accordance with this disclosure.Although aspects, features, and elements are described herein inparticular combinations, each aspect, feature, or element may be usedindependently or in various combinations with or without other aspects,features, and elements.

Although some embodiments herein refer to methods, it will beappreciated by one skilled in the art that they may also be embodied asa system or computer program product. Accordingly, aspects of thepresent invention may take the form of an entirely hardware embodiment,an entirely software embodiment (including firmware, resident software,micro-code, etc.) or an embodiment combining software and hardwareaspects that may all generally be referred to herein as a “processor,”“device,” or “system.” Furthermore, aspects of the present invention maytake the form of a computer program product embodied in one or morecomputer readable mediums having computer readable program code embodiedthereon. Any combination of one or more computer readable mediums may beutilized. The computer readable medium may be a computer readable signalmedium or a computer readable storage medium. A computer readablestorage medium may be, for example, but not limited to, an electronic,magnetic, optical, electromagnetic, infrared, or semiconductor system,apparatus, or device, or any suitable combination of the foregoing. Morespecific examples (a non-exhaustive list) of the computer readablestorage medium include the following: an electrical connection havingone or more wires, a portable computer diskette, a hard disk, a randomaccess memory (RAM), a read-only memory (ROM), an erasable programmableread-only memory (EPROM or Flash memory), an optical fiber, a portablecompact disc read-only memory (CD-ROM), an optical storage device, amagnetic storage device, or any suitable combination of the foregoing.In the context of this document, a computer readable storage medium maybe any tangible medium that can contain or store a program for use by orin connection with an instruction execution system, apparatus, ordevice.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including, but not limited to,electro-magnetic, optical, or any suitable combination thereof. Acomputer readable signal medium may be any computer readable medium thatis not a computer readable storage medium and that can communicate,propagate, or transport a program for use by or in connection with aninstruction execution system, apparatus, or device.

Program code embodied on a computer readable medium may be transmittedusing any appropriate medium, including but not limited to CDs, DVDs,wireless, wireline, optical fiber cable, RF, etc., or any suitablecombination of the foregoing.

Computer program code for carrying out operations for aspects of thepresent invention may be written in any combination of one or moreprogramming languages, including an object-oriented programming languagesuch as Java, Smalltalk, C++ or the like and conventional proceduralprogramming languages, such as the “C” programming language or similarprogramming languages. The program code may execute entirely on theuser's computer, partly on the user's computer, as a stand-alonesoftware package, partly on the user's computer and partly on a remotecomputer or entirely on the remote computer or server. In the latterscenario, the remote computer may be connected to the user's computerthrough any type of network, including a local area network (LAN) or awide area network (WAN), or the connection may be made to an externalcomputer (for example, through the Internet using an Internet ServiceProvider).

Attributes may comprise any data characteristic, category, content, etc.that in one example may be non-quantifiable or non-numeric. Measures maycomprise quantifiable numeric values such as sizes, amounts, degrees,etc. For example, a first column containing the names of states may beconsidered an attribute column and a second column containing thenumbers of orders received for the different states may be considered ameasure column.

Aspects of the present embodiments are described above with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems) and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer program instructions. These computer program instructions maybe provided to a processor of a computer, such as a special purposecomputer, or other programmable data processing apparatus to produce amachine, such that the instructions, which execute via the processor ofthe computer or other programmable data processing apparatus, createmeans for implementing the functions/acts specified in the flowchartand/or block diagram block or blocks. These computer programinstructions may also be stored in a computer readable medium that candirect a computer, other programmable data processing apparatus, orother devices to function in a particular manner, such that theinstructions stored in the computer readable medium produce an articleof manufacture including instructions which implement the function/actspecified in the flowchart and/or block diagram block or blocks. Thecomputer program instructions may also be loaded onto a computer, otherprogrammable data processing apparatus, or other devices to cause aseries of operational steps to be performed on the computer, otherprogrammable apparatus or other devices to produce a computerimplemented process such that the instructions which execute on thecomputer or other programmable apparatus provide processes forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. The flowcharts and block diagrams in thefigures illustrate the architecture, functionality, and operation ofpossible implementations of systems, methods and computer programproducts according to various embodiments of the present invention. Inthis regard, each block in the flowchart or block diagrams may representa module, segment, or portion of code, which comprises one or moreexecutable instructions for implementing the specified logicalfunction(s). It should also be noted that, in some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts, or combinations of special purpose hardware andcomputer instructions.

While the disclosure has been described in connection with certainembodiments, it is to be understood that the disclosure is not to belimited to the disclosed embodiments but, on the contrary, is intendedto cover various modifications and equivalent arrangements includedwithin the scope of the appended claims, which scope is to be accordedthe broadest interpretation so as to encompass all such modificationsand equivalent structures as is permitted under the law.

What is claimed is:
 1. A method for database replay log compactionverification, comprising: identifying at least one replay log of atable, the at least one replay log comprising first databasemanipulation commands; obtaining a compacted replay log of the table,the compacted replay log comprising second database manipulationcommands, wherein the second database manipulation commands are insertcommands, and wherein an insert command of the first databasemanipulation commands includes a column and a corresponding value forthe column; replaying, to obtain a first replay result, the firstdatabase manipulation commands, wherein replaying the first databasemanipulation commands comprises: identifying condition columns of thetable, wherein the condition columns are used in at least one conditionof the first database manipulation commands; obtaining a rowcorresponding to the insert command, wherein obtaining the rowcorresponding to the insert command comprises: including in the row oneof a modified value of the corresponding value of the column or thecorresponding value based on whether the condition columns include thecolumn of the insert command; adding the row to the first replay result;replaying, to obtain a second replay result, the second databasemanipulation commands, wherein replaying the second databasemanipulation commands comprises: determining whether to include in rowsof the second replay result modified values of values of columns basedon the condition columns; and responsive to one row of the first replayresult not matching a corresponding row of the second replay result,sending a notification including a non-match.
 2. The method of claim 1,wherein the column is of type string.
 3. The method of claim 2, whereinthe modified value is a hash of the corresponding value.
 4. The methodof claim 1, wherein the column is a first column and the correspondingvalue is a first value, wherein the insert command further includes asecond column and a second corresponding value for the second column,further comprising: responsive to the second column being of a typeother than string, including the second corresponding value in the row.5. The method of claim 1, wherein adding the row to the first replayresult comprises: adding a hash value of the row to the first replayresult.
 6. The method of claim 1, further comprising: comparing a firsthash value of the one row of the first replay result to a second hashvalue of the corresponding row of the second replay result to determinewhether the one row of the first replay result matches the correspondingrow of the second replay result.
 7. The method of claim 1, whereinreplaying, to obtain a second replay result, the second databasemanipulation commands comprises: obtaining the corresponding row of thesecond replay result; and responsive to the one row matching thecorresponding row of the second replay result, obtaining another row ofsecond replay result for comparing to a corresponding row of the firstreplay result.
 8. The method of claim 1, wherein the compacted replaylog is a compacted version of the at least one replay log that has beengenerated using a compaction process.
 9. A method for replay logcompaction verification, comprising: replaying, to obtain a first replayresult, first database manipulation commands of at least one replay log,wherein the first database manipulation commands comprises at least oneof an update command or a delete command; replaying, to obtain a secondreplay result, second database manipulation commands of a compactedreplay log, wherein the second database manipulation commands are insertcommands, and wherein the compacted replay log is a compacted version ofthe first replay log that has been generated using a compaction process;and responsive to a row of the first replay result not matching acorresponding row of the second replay result, sending a notificationindicating a non-match corresponding to the row of the first replayresult.
 10. The method of claim 9, further comprising: responsive to thefirst replay result matching the second replay result, deleting the atleast one replay log.
 11. The method of claim 9, wherein replaying, toobtain a first replay result, the first database manipulation commandsof at least one replay log comprises: identifying condition columns ofthe first database manipulation commands; and replacing a first value ofa field included in a first command in the first database manipulationcommands with a hash value responsive to a determination that the fieldis not included in the condition columns.
 12. The method of claim 9,further comprising: obtaining a first hashed row value for the row ofthe first replay result; and obtaining a second hashed row value for thecorresponding row of the second replay result.
 13. The method of claim12, further comprising: comparing the first hashed row value and thesecond hashed row value to determine whether the row of the first replayresult matches the corresponding row of the second replay result.
 14. Adevice, comprising: a memory; and a processor, the processor configuredto execute instructions stored in the memory for database replay logcompaction verification, the instructions comprise instructions to:identify at least one replay log of a table, the at least one replay logcomprising first database manipulation commands; obtain a compactedreplay log of the table, the compacted replay log comprising seconddatabase manipulation commands, wherein the second database manipulationcommands are insert commands, and wherein an insert command of the firstdatabase manipulation commands includes a column and a correspondingvalue for the column; replay, to obtain a first replay result, the firstdatabase manipulation commands, wherein to replay the first databasemanipulation commands comprises to: identify condition columns of thetable, wherein the condition columns are used in at least one conditionof the first database manipulation commands; obtaining a rowcorresponding to the insert command, wherein obtaining the rowcorresponding to the insert command comprises: responsive to thecondition columns not including the column of the insert command,including a modified value of the corresponding value of the column inthe row; and responsive to the condition columns including the column ofthe insert command, including the corresponding value of the column inthe row; add the row to the first replay result; replay, to obtain asecond replay result, the second database manipulation commands; andresponsive to one row of the first replay result not matching acorresponding row of the second replay result, send a notificationincluding a non-match.
 15. The device of claim 14, wherein the column isof type string.
 16. The device of claim 15, wherein the modified valueis a hash of the corresponding value.
 17. The device of claim 14,wherein the column is a first column and the corresponding value is afirst value, wherein the insert command further includes a second columnand a second corresponding value for the second column, and wherein theprocessor is further configured to execute instructions stored in thememory to: responsive to the second column being of a type other thanstring, include the second corresponding value in the row.
 18. Thedevice of claim 14, wherein to add the row to the first replay resultcomprises to: add a hash value of the row to the first replay result.19. The device of claim 14, wherein the processor is further configuredto execute instructions stored in the memory to: compare a first hashvalue of the one row of the first replay result to a second hash valueof the corresponding row of the second replay result to determinewhether the one row of the first replay result matches the correspondingrow of the second replay result.
 20. The device of claim 14, wherein toobtain the second replay result, the second database manipulationcommands comprises to: obtain the corresponding row of the second replayresult; and responsive to the one row matching the corresponding row ofthe second replay result, obtain another row of second replay result forcomparing to a corresponding row of the first replay result.